IoT for Elderly, Aging and eHealth: Quality of Life and Independent Living for the Elderly (Lecture Notes on Data Engineering and Communications Technologies) 3030933865, 9783030933869

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Lecture Notes on Data Engineering and Communications Technologies 108

C. H. Wu · Cathy H. Y. Lam · Fatos Xhafa · Valerie Tang · W. H. Ip

IoT for Elderly, Aging and eHealth Quality of Life and Independent Living for the Elderly

Lecture Notes on Data Engineering and Communications Technologies Volume 108

Series Editor Fatos Xhafa, Technical University of Catalonia, Barcelona, Spain

The aim of the book series is to present cutting edge engineering approaches to data technologies and communications. It will publish latest advances on the engineering task of building and deploying distributed, scalable and reliable data infrastructures and communication systems. The series will have a prominent applied focus on data technologies and communications with aim to promote the bridging from fundamental research on data science and networking to data engineering and communications that lead to industry products, business knowledge and standardisation. Indexed by SCOPUS, INSPEC, EI Compendex. All books published in the series are submitted for consideration in Web of Science.

More information about this series at https://link.springer.com/bookseries/15362

C. H. Wu · Cathy H. Y. Lam · Fatos Xhafa · Valerie Tang · W. H. Ip

IoT for Elderly, Aging and eHealth Quality of Life and Independent Living for the Elderly

C. H. Wu The Hang Seng University of Hong Kong Shatin, Hong Kong

Cathy H. Y. Lam The Hang Seng University of Hong Kong Shatin, Hong Kong

Fatos Xhafa Technical University of Catalonia Barcelona, Spain

Valerie Tang The Hang Seng University of Hong Kong Shatin, Hong Kong

W. H. Ip The Hong Kong Polytechnic University Hung Hom, Hong Kong

ISSN 2367-4512 ISSN 2367-4520 (electronic) Lecture Notes on Data Engineering and Communications Technologies ISBN 978-3-030-93386-9 ISBN 978-3-030-93387-6 (eBook) https://doi.org/10.1007/978-3-030-93387-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Prof Xhafa dedicates this book to the memory of his late mother.

Foreword

Population aging is an inevitable phenomenon characterizing modern society that comes from the decline in fertility and improvement in survival. It results in a real demographic transition with strong implications on healthcare industry. As a consequence, IoT, big data analytics and Artificial Intelligence technologies are starting to be widely used for delivering more and more accurate and responsive healthcare services to elderly. One of the critical dilemmas is the gap between everyday practice and the best and innovative one. Many fail to understand the relationship between straightforward approaches that work well only in appropriate circumstances and more sophisticated approaches that pay big dividends when the aspects they focus on deserve particular attention. Accordingly, this book is helpful for readers coming from both research and industry in addressing the elderly needs with simplicity and completeness, without being simplistic in a direct manner. The reported case studies are useful for getting started and dealing with different situations. Some more sophisticated approaches with state-of-the-art technologies are also addressed. This book also provides straightforward concepts as a starting position without overlooking their limitations and addresses many of the implications of more successful cases later. The book follows a very practical approach, dedicated to all readers which would like to immediately understand all the insights of this promising technological sector. It is a stimulating source for those with experience in this field but can be also helpful for novices in this emerging interdisciplinary research area to integrate and innovate healthcare technologies for the elderly. This book will also be a good choice for educators who want to bring specific case studies into their classrooms. Francesco Palmieri Professor of Computer Networks Cybersecurity and IoT, Department of Computer Science University of Salerno Fisciano, Italy

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Preface

The speedily aging population leads to a continuously increasing demand for elderly healthcare services in terms of capacity and quality. However, there is a lack of resources provided by elderly homes, daycare centers and hospitals. Many domestic healthcare services are unable to take care of all of the elderly. The implementation of Internet of Things (IoT) infrastructures and cloud solutions is everywhere since the rapid evolution of the Internet. Different types of enabling technologies are applied to design, develop and implement various IoT-based and/or cloud-based healthcare platforms for the elderly on healthcare services. This book project drives the aforementioned hunger for existing technologies, including Cloud/Fog computing, cloud ecosystem, specific edge devices and related software with IoT technologies. This book explores research outputs in adopting such advanced technologies in the healthcare industry from theoretical, empirical and practical perspectives. In this book, 11 chapters were included, which provide a comprehensive review of the healthcare industry’s overview and discussion on new interdisciplinary research directions that have become possible in healthcare areas. It also establishes a showcase focusing on the possible ways to improve the quality of life and independent living for the elderly. It is expected to introduce new and practical aging in place applications worldwide. This book includes several case studies which combine the scientific research, the knowledge of healthcare professionals and manufacturing concerns of the industry to illustrate different solutions and applications that are practical and innovative. Shatin, Hong Kong Shatin, Hong Kong Barcelona, Spain Shatin, Hong Kong Hung Hom, Hong Kong

C. H. Wu Cathy H. Y. Lam Fatos Xhafa Valerie Tang W. H. Ip

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Acknowledgments

The work described in this book was partially supported by a matching grant from the University Grants Committee of the Hong Kong Special Administrative Region, China (RMGS Project No. 700005), and a funded project under the Innovation and Technology Support Programme from the Innovation and Technology Fund of the Hong Kong Special Administrative Region, China (Project Code: ITS/383/15FX). The close collaboration and support to V-Care Development Ltd. and Kowloon East PHAB Centre of Hong Kong PHAB Association are greatly appreciated. Fatos Xhafa’s work is supported by Spanish Ministry of Science, Innovation and Universities, Programme Estancias de profesores e investigadores senior en centros extranjeros, incluido el Programa Salvador de Madariaga 2019, PRX19/00155. Work partially done at the University of Surrey, U.K., on leave from the Technical University of Catalonia, BarcelonaTech. Discussions with Prof. Paul Krause on various interesting topics of this book are highly appreciated. We wish to express our gratitude to Dr. Polly Leung, Ms. April Lam and Mr. Rodney Wong for sharing their rich knowledge and providing valuable advice on our work as a researcher in nursing homes, an experienced nurse in elderly care and a professional physiotherapist, respectively, and also Jennifer Sweety Johnson, Suresh Dharmalingam and Thomas Ditzinger of Springer for help with this book production.

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Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 3

2

The Vision of the Healthcare Industry for Supporting the Aging Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Healthcare Needs of the Ageing People . . . . . . . . . . . . . . . . . . . . . . 2.2 Elderly Healthcare System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Short-Term Healthcare Service . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Long-Term Healthcare Service . . . . . . . . . . . . . . . . . . . . . . . 2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 5 7 8 9 14 15

3

Building Long-Term Care Services Around the World . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Ageing Policies in Taiwan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Ageing Policies in Hong Kong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Ageing Policies in Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Ageing Policies in the United States . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17 17 17 19 22 23 24 25

4

IOT and Cloud Computing for Development of Systems for Elderly and eHealth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Integration of IoT Technologies and Cloud Computing in Health Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 IoT Technologies and Cloud Computing in Health Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Application of Remote Health Monitoring . . . . . . . . . . . . . 4.1.3 Benefits of Remote Health Monitoring . . . . . . . . . . . . . . . . 4.2 Health Information Management System and eHealth . . . . . . . . . 4.2.1 Health Information System . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 eHealth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27 27 27 30 32 33 33 35

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4.3 Challenges for Health Information System and eHealth . . . . . . . . 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

6

New Generation of Healthcare Services Based on Internet of Medical Things, Edge and Cloud Computing Infrastructures . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Related Work, Internet of Medical Things and Stream Processing Engines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Offloading Computation from Cloud to Edge . . . . . . . . . . 5.2.2 The Intelligent Edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Design of Wearables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Semantic Edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.5 Stream Processing Engines . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 A Generic Type Architecture of Healthcare Applications Based on IoMT, Edge and Cloud Computing . . . . . . . . . . . . . . . . . 5.3.1 Sensing Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Pre-processing Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Cluster/Fog Processing Layer . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Persistence Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.5 Application Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 IoMT Data Stream Processing for Anomaly Detection with Application to Heart Diseases and Real Data Sets . . . . . . . . . 5.4.1 Hierarchical Temporal Memory Algorithm (HTM) . . . . . . 5.4.2 REALDISP Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Conclusions and Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Artificial Intelligence and Data Mining Techniques for the Well-Being of Elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Artificial Intelligence Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Case-Based Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Analytic Hierarchy Process (AHP) . . . . . . . . . . . . . . . . . . . 6.3 Data Mining Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Association Rule Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36 37 37 39 39 40 40 41 41 41 42 43 43 43 44 45 45 45 46 46 47 49 51 51 51 52 54 55 58 58 59 61 62 63 63

Contents

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7

Domesticating Homecare Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Research Methodology of Domesticating Homecare Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Front-End Homecare Service Request Module . . . . . . . . . 7.2.2 Back-End Homecare Service Planning Module . . . . . . . . . 7.3 Case Study in a Local Domestic Homecare Service Center . . . . . 7.3.1 Data Extraction and Data Processing . . . . . . . . . . . . . . . . . . 7.3.2 Development of the Genetic Algorithm Model for Work Plan Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67 67

8

Case Study in Fall Prevention in Indoor Environments . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Data Collection and Consultation Module . . . . . . . . . . . . . 8.2.2 Balance Board Development Module . . . . . . . . . . . . . . . . . 8.2.3 Health Platform Development Module . . . . . . . . . . . . . . . . 8.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Training and Rehabilitation . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87 87 88 88 89 93 93 94 94 95 95 97 98

9

Case Study in Elderly Consultancy Services . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Cloud-Based Data Collection Module . . . . . . . . . . . . . . . . . 9.2.2 Care Solution Generation Module . . . . . . . . . . . . . . . . . . . . 9.2.3 Solution Evaluation Module . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Problems in the Case Company . . . . . . . . . . . . . . . . . . . . . . 9.3.2 Implementation of the Proposed ICPS in the Case Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.1 Improvement in the Efficiency of Care Plan Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.2 Performance in Feedback Collection . . . . . . . . . . . . . . . . . . 9.4.3 Improvement in Service Satisfaction . . . . . . . . . . . . . . . . . . 9.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

99 99 100 100 100 102 103 104

68 68 70 75 76 79 81 84 85

105 108 109 109 110 110 111

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Contents

10 Case Study in Remote Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 IoT Based Healthcare Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

113 113 115 119 122 122 123

Conclusions and Future Directions of Research . . . . . . . . . . . . . . . . . . . . . . . 125 Acronyms and Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

About the Authors

C. H. Wu Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong Hang Shin Link, Siu Lek Yuen, Shatin, N.T., Hong Kong SAR Dr. Wu excels in his role with The Hong Kong Polytechnic University (PolyU) as a researcher with more than a decade of professional experience. Having held numerous roles with the university previously, his expertise precedes him. Over the years, he worked closely with many industrial partners to support and contribute to formulating their business strategies, operations improvement and new product development. He received the Big Data Analysis Award from the China & Hong Kong Enterprise Market Development Association in 2014, the Best Reviewer Award for a journal entitled Internet Researchin 2016 and the Top Peer Reviewer on Publons in 2019. He regularly contributes to research papers in Industrial Internet of Things (IIoT), engineering optimisation and digital health. In collaboration with several scholars at PolyU, Tianjin University, Sun Yat-sen University, The University of York, etc., his project and research outcomes have been presented in 15+ international conferences and published in 70+ international refereed journals, such as Information Science, Internet Research, International Journal of Production Research, IEEE Communication Letters, Enterprise Information Systems, Expert Systems with Applications and Industrial Management and Data Systems. He is a member of IEEE and a senior member of the Hong Kong Society of Quality. Additionally, he serves on the editorial boards of various international xvii

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About the Authors

journals under Taylor & Francis, SAGE, IGI Global and MDPI. You can find more information on his SCIE/SSCI publication and his peer-review services in his Publons page: https://publons.com/researcher/1224587/ch-wu. Cathy H. Y. Lam Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong Hang Shin Link, Siu Lek Yuen, Shatin, N.T., Hong Kong SAR Dr. Lam is currently an Assistant Professor at The Hang Seng University of Hong Kong. She received her Ph.D. from the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University. During the years of research, over 80 international journals and conference papers were published in the areas of supply chain and logistics management, data analytics in supply chain, e-commerce logistics, healthcare logistics, IoT applications as well as artificial intelligence. Among them, 35 journal papers are SCI/SSCI listed, while some of them were published in wellknown journals such as Applied Soft Computing, IEEE Transactions on Vehicular Technology, International Journal of Production Economics, International Journal of Production Research, Industrial Management and Data Systems, Expert Systems with Application and Internet Research. She received the Outstanding Paper Award and the Highly Commented Award of Emerald Literati Network Awards in 2019. Currently, she serves as the Editorial Board Member of the International Journal of Engineering Business Management, and is the member of Hong Kong Logistics Association and Institute of Industrial and Systems Engineers.

About the Authors

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Fatos Xhafa Department of Computer Science, Technical University of Catalonia, BarcelonaTech, Campus Nord, Ed. Omega, C/Jordi Girona 1-3, 08034 Barcelona, Spain Fatos Xhafa, Ph.D. in Computer Science, is Full Professor at the Technical University of Catalonia (UPC), Barcelona, Spain. He has held various tenured and visiting professorship positions. He was a Visiting Professor at the University of Surrey, U.K. (2019/2020); Visiting Professor at the Birkbeck College, University of London, U.K. (2009/2010) and a Research Associate at Drexel University, Philadelphia, USA (2004/2005). He was a Distinguished Guest Professor at Hubei University of Technology, China, for the duration of 3 years (2016– 2019). He has widely published in peer-reviewed international journals, conferences/workshops, book chapters, and edited books and proceedings in the field (Hindex 55). He is awarded teaching and research merits by Spanish Ministry of Science and Education, by IEEE conferences and best paper awards. He has an extensive editorial service. He is Founder and Editor-In-Chief of Internet of Things—Journal—Elsevier (Scopus and Clarivate WoS Science Citation Index) and of International Journal of Grid and Utility Computing, (Emerging Sources Citation Index) and AE/EB Member of several indexed Int’l Journals. He is Founder and Editor-inChief of two book series: the Springer Book Series Lecture Notes in Data Engineering and Communication Technologies (SCOPUS, EI Compendex, ISI WoS) and the Elsevier Book Series Intelligent Data-Centric Systems (SCOPUS, EI Compendex). He is a member of IEEE Communications Society, IEEE Systems, Man & Cybernetics Society and Founder Member of Emerging Technical Subcommittee of Internet of Things. His research interests include IoT and cloud-tothing continuum computing, massive data processing and collective intelligence, optimization, security and trustworthy computing and machine learning, among others. He can be reached at [email protected]. Please visit also http://www.cs.upc.edu/~fatos/and at http:// dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos.

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About the Authors

Valerie Tang Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong Hang Shin Link, Siu Lek Yuen, Shatin, N.T., Hong Kong SAR Dr. Tang is currently a Postdoctoral Fellow in the Department of SCM at the Hang Seng University of Hong Kong. She received her Ph.D. in Department of Industrial and Systems Engineering from the Hong Kong Polytechnic University. Her current research areas cover Artificial Intelligence, Healthcare Services Planning, Internet of Things, Logistics and Supply Chain Management. W. H. Ip Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada Prof. Andrew W. H. Ip received his Ph.D. from Loughborough University (U.K.), MBA from Brunel University (U.K.), MSc in Industrial Engineering from Cranfield University (U.K.) and LLB (Hons) from the University of Wolverhampton (U.K.). He is now Professor Emeritus of Mechanical Engineering of University of Saskatchewan, and Senior Research Fellow in the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University. He has published over 300 papers with over 200 papers in SCI-indexed journals and has written books and invited book chapters. He is the Editor-in-Chief of Enterprise Information Systems, Taylor & Francis; the Editor-in-Chief and Founder of the International Journal of Engineering Business Management, SAGE; the Editor-in-Chief of International Journal of Software Science and Computational Intelligence and an editorial member of various international journals. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), member of the Hong Kong Institution of Engineers (HKIE) and Honorary Chairman of IISE (HK Chapter). More information and publications can be found at https://www.researchgate.net/pro file/Wh-Ip.

List of Figures

Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4

Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 6.5

a Percentage of population aged 65 Years or above in 2020. b Percentage of population aged 65 years or above in 2050 . . . . Overview of the structure of elderly healthcare service system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reasons for elderly of planning to stay at home as grow older . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Institutional healthcare services in basic healthcare services. b Institutional healthcare services in supportive services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework of the ABC network [7] . . . . . . . . . . . . . . . . . . . . . . . Vision, mission, overarching principles and strategic directions in ESPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elderly care service delivery model [12] . . . . . . . . . . . . . . . . . . . . Structure of the long-term care insurance system in Japan [18] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wearable devices network in elderly healthcare monitoring . . . . Two-way computing in edge computing [4] . . . . . . . . . . . . . . . . . Structure of the elderly health monitoring mechanism . . . . . . . . . A generic layered architecture for IoMT data stream processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CPU utilization and memory consumption on Raspberry Pi node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CPU utilization and memory consumption on the layers of the system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results on anomaly detection for different thresholds and resolution values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process of a fuzzy logic system . . . . . . . . . . . . . . . . . . . . . . . . . . . Fuzzy model with multi-layers [9] . . . . . . . . . . . . . . . . . . . . . . . . . Four steps in CBR process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework for GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . AHP hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 8 11

13 19 20 21 22 28 29 30 44 47 48 49 52 53 54 56 57 xxi

xxii

Fig. 6.6 Fig. 6.7 Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4 Fig. 7.5 Fig. 7.6 Fig. 7.7 Fig. 7.8 Fig. 7.9 Fig. 7.10 Fig. 7.11 Fig. 8.1 Fig. 8.2 Fig. 8.3 Fig. 8.4 Fig. 8.5 Fig. 8.6 Fig. 8.7 Fig. 8.8 Fig. 9.1 Fig. 9.2 Fig. 9.3 Fig. 9.4 Fig. 9.5 Fig. 10.1 Fig. 10.2 Fig. 10.3 Fig. 10.4 Fig. 10.5 Fig. 10.6 Fig. 10.7 Fig. 10.8

List of Figures

An example of the kNN classification [50] . . . . . . . . . . . . . . . . . . An Apriori algorithm with a 1-itemset . . . . . . . . . . . . . . . . . . . . . The system architecture of the DHSPS . . . . . . . . . . . . . . . . . . . . . The example of the service time matrix . . . . . . . . . . . . . . . . . . . . . Chromosome encoding for service delivery planning . . . . . . . . . An example of multi-point crossover in the GA . . . . . . . . . . . . . . An example of multi-point mutation in the GA . . . . . . . . . . . . . . Geographic locations of all service requests . . . . . . . . . . . . . . . . . Excel VBA code for travelling time calculation . . . . . . . . . . . . . . Matrix of travelling time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An example of chromosome encoding . . . . . . . . . . . . . . . . . . . . . Sample result of service delivery plan generated for a Route 1, and b Route 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Travel sequence and the total time of Route 1 . . . . . . . . . . . . . . . Design of balance board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Position of six infra-red detectors in the balance board . . . . . . . . Distance detection in feet movement . . . . . . . . . . . . . . . . . . . . . . . Architecture for cloud computing between balance boards and cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . User interface for registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . User interface to display the test results . . . . . . . . . . . . . . . . . . . . User interface for the score page . . . . . . . . . . . . . . . . . . . . . . . . . . User interface for the exercise levels summary . . . . . . . . . . . . . . . Architecture of the ICPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Operation flow of the ICPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision tree structure for case retrieval . . . . . . . . . . . . . . . . . . . . User interface for case reuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interface of the database for storing care plans with high quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shift of healthcare models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evolution of eHealth applications . . . . . . . . . . . . . . . . . . . . . . . . . Architecture of the prosper IoT-based healthcare model . . . . . . . The flow of knowledge building . . . . . . . . . . . . . . . . . . . . . . . . . . . An example for remote medical diagnosis . . . . . . . . . . . . . . . . . . An example for healthcare knowledge consultation . . . . . . . . . . . An example for self-healthcare management . . . . . . . . . . . . . . . . Data flow for self-healthcare management . . . . . . . . . . . . . . . . . .

61 62 69 70 72 74 74 77 78 78 80 84 84 91 92 92 93 94 95 96 96 101 105 106 107 108 114 114 115 118 120 120 121 121

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 3.1 Table 4.1 Table 4.2 Table 4.3 Table 5.1 Table 6.1 Table 6.2 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 7.6 Table 7.7 Table 7.8 Table 7.9 Table 7.10 Table 8.1 Table 8.2 Table 9.1 Table 9.2 Table 9.3 Table 9.4

Mean time to walk four meters (Seconds) [5] . . . . . . . . . . . . . . Discharge destination for elderly versus non-elderly (%) [13] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Population of the elderly in 2014, 2030 and 2064 [18] . . . . . . . Types and Features of Institutional Healthcare Services for Elders in Hong Kong [19] . . . . . . . . . . . . . . . . . . . . . . . . . . . Differences of the services provided between Long-term Care Plans 1.0 and 2.0 [6] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Information for better decision making in healthcare [15] . . . . Categories for the measurement of HIS performance [14] . . . . Measures for improving eHealth in WHO member states [15] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A comparison among different frameworks . . . . . . . . . . . . . . . . K-means algorithm example . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-mean clustering results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Notation table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elderly services provided by the service centre . . . . . . . . . . . . . Examples of data from the data sources . . . . . . . . . . . . . . . . . . . The coordinates of the first 15 service locations . . . . . . . . . . . . . Sample service requests by the elderly . . . . . . . . . . . . . . . . . . . . The GA parameter settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The combinations of settings in the GA generation . . . . . . . . . . GA result of population size = 50 . . . . . . . . . . . . . . . . . . . . . . . . GA result of population size = 100 . . . . . . . . . . . . . . . . . . . . . . . Summary of average values of different parameters . . . . . . . . . Posture for the balance workout for a sitter . . . . . . . . . . . . . . . . Posture for the balanced workout for a walker . . . . . . . . . . . . . . Example of clustering before adding new care solution . . . . . . Clustering results after adding new care solution . . . . . . . . . . . . Information of the new applicant . . . . . . . . . . . . . . . . . . . . . . . . . Improvement in the performance of care planning . . . . . . . . . . .

7 10 13 14 18 34 34 35 43 60 61 70 75 76 77 79 80 81 82 83 83 89 90 103 104 107 109 xxiii

xxiv

Table 9.5 Table 10.1

List of Tables

Improvement in service satisfaction . . . . . . . . . . . . . . . . . . . . . . Comparison between wireless LAN, Bluetooth and Zigbee . . .

110 117

Chapter 1

Introduction

Due to the demographic change and longer life expectancies, the ageing population is a profound and pervasive problem worldwide, such as Japan, China, the United States, and Taiwan. It is forecasted that this phenomenon will continuously increase from 9.3% of people aged 65+ in 2019 to 16% of people aged 65+ in 2050 [1]. Associated with the ageing population, such elderly may suffer from different kinds of chronic non-communicable diseases or poor mental/physical disabilities conditions. This global leads to the increasing demands for healthcare services, whether short-term healthcare services in hospitals or long-term community care offered by various long-term care services providers. To deliver quality, affordable and accessible healthcare services to the elderly, governments in various countries seek to deploy the concept of smart health to strengthen existing healthcare systems. Smart health is defined as integrating ubiquitous computing, big data analytics, and artificial intelligence to achieve health and diseases diagnosis, health monitoring, health data analysis, and prediction [2]. Smart health emphasises the use of internet of things (IoT) technology and a state of thinking for improving healthcare facilities in longterm and short-term healthcare services. In smart health, many biomedical sensors are connected in the healthcare system to collect various health data such as temperature, heart rate, blood pressure and breathing rate. Through wireless communication technologies such as WiFi and Bluetooth, relevant data can be collected and centralised to cloud databases for achieving different applications through big data analytics and A.I. techniques. Considering the increase of healthcare demands worldwide, the adoption of IoT, big data analytics, and A.I. techniques are worthy of consideration in the healthcare industry to deliver accurate and responsive healthcare services to the elderly. Therefore, this book aims to study and explore research outputs in adopting such advanced technologies in the healthcare industry from theoretical, empirical, and practical perspectives. In this book, eleven chapters were included, which provides a comprehensive review of the healthcare industry’s overview and discussion on new interdisciplinary research directions that have become possible in the field of healthcare areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_1

1

2

1 Introduction

This chapter briefly introduces the current problems raised by the ageing population problems. After that, the objective of this book will be highlighted to discuss the needs of the adoption of IoT, big data analytics and A.I. technologies in the healthcare industry to support existing healthcare systems around the world. In Chap. 2, the vision of the healthcare industry for supporting the ageing population will be presented. In general, two kinds of elderly healthcare systems, i.e. short-term healthcare service and long-term healthcare service, are reviewed to describe their roles and significance in the healthcare industry. Chapter 3 provides the overview, features, and differences of long-term care healthcare policies in various countries. Establishing the comprehensive healthcare framework/model can facilitate coordination among various healthcare parties and hence better allocate resources, i.e. staffing, equipment, and facilities, to deliver timely and accurate healthcare services. In Chaps. 4–5, these two chapters discuss the infrastructure and elements and its applications of IoT/IoMT, edge and cloud computing techniques in the healthcare industry. In addition, considering the fast development in Edge computing technologies, it allows the implementation and decentralised deployment of healthcare systems at a large scale, which benefits the digital cloud ecosystem. However, as healthcare organisations without sufficient knowledge, integrating these new paradigms and related technologies poses many challenges in design, implementation, data integration and data privacy aspects. In Chap. 6, A.I. and data mining techniques for the well-being of the elderly will be presented. Three A.I. techniques, including fuzzy logic, case-based reasoning, genetic algorithm and analytic hierarchy process, are reviewed. In comparison, three data mining techniques, including clustering, classification and association rule mining, are studied. The adoption of data mining and A.I. techniques can provide data insight for healthcare stakeholders to uncover hidden connections and provide recommendations for decision-making to mitigate health risks and harm. Chapters 7–10 cover the case studies implementing advanced technologies in various healthcare areas, i.e. domesticating homecare services, fall prevention, elderly consultancy services, and remote diagnosis. It was found that the adoptions of the advanced technologies offer benefits to the healthcare stakeholders in the areas of patient health management and monitoring, health/disease prediction and diagnosis and healthcare resources allocations. Furthermore, a conclusion of this book and future research directions in the healthcare industry are given at the end. With the rapid growth of advanced technologies applications in healthcare areas, it is believed that reliable and efficient healthcare services can be delivered to the elderly to maintain their health.

References

3

References 1. World Health Organization. (2020). “Ageing”, in Health at a Glance: Asia/Pacific 2020: Measuring Progress Towards Universal Health Coverage. OECD Publishing. 2. Shen, X. L., Li, Y. J., & Sun, Y. (2018). Wearable health information systems intermittent discontinuance: A revised expectation-disconfirmation model. Industrial Management & Data Systems, 118(3), 506–523.

Chapter 2

The Vision of the Healthcare Industry for Supporting the Aging Population

2.1 Healthcare Needs of the Ageing People Population ageing is a global issue found in many countries. This phenomenon is expected to continue, even accelerate, due to decreased fertility and the increase in life expectancy [1]. According to the projection by United Nations (2019), the proportion of ageing people (age 65 or above) would nearly double from 9.3% in 2020 to 16% in 2050. In 2020, only the proportion of the elderly in Japan exceeded 30% (Fig. 2.1a). However, by the middle of the century, many countries, including Canada, China and most of the countries in Europe, will reach the proportion of 30% or above, as shown in Fig. 2.1b. In addition, it is projected that among the ageing population, the average burden of chronic non-communicable diseases, when comparing to other types of illness (such as infectious diseases, nutritional conditions, and injuries), would increase significantly to account for more than 87% of the healthcare burden in all the nations in 2030. The chronic disabling non-communicable diseases may cause functional disabilities with temporary or permanent limitations and diminish the quality of life for ageing people. Consequently, this effect would be magnified under the global shift of illness from acute life-threatening infectious diseases to chronic disabling non-communicable diseases [2]. The elderly may suffer more severely from chronic diseases due to the decline in intrinsic capacity when they age. Thus, older adults’ health conditions tend to be more chronic and complex. As described by WHO [3], ageing can be “characterised by a gradual, lifelong accumulation of molecular and cellular damage that results in a progressive, generalised impairment in many body functions, an increased vulnerability to environmental challenges and a growing risk of disease and death”. Some of the elderly are suffering from functions declining such as movement, sensory, or cognitive ability; some others of the same age may be in excellent condition of

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_2

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Fig. 2.1 a Percentage of population aged 65 Years or above in 2020. b Percentage of population aged 65 years or above in 2050

health. Although condition changes may vary significantly among the elderly, ageing always results in a gradual decline in physical capacity and an increased risk of being infected by diseases. Specifically, for ageing people, their bones tend to be less dense, and weaker bones are more likely to break, and their joints may become thin and more susceptible to injury due to the wear and tear of years of the movement. In the meantime, their muscles mass and strength tend to decline; although regular exercise may significantly delay the process, statistics show that the elderly need two weeks of exercise to make up the muscle mass lost during each day of strict bed rest [4]. Their fading functions of organs such as eyes, ears, mouth, and nose may also cause troubles in their lives. With the example of ‘gait speed (time needed to walk four meters)’ as shown in Table 2.1, statistics from Mexico indicates that as people age, the gait speed declines with the decreasing rate overgrows for the more senior age group of “70–79” [5]. In addition, it could be noticed from the table that in the age group of 80+, their required time to walk four meters almost double compared to the time of

2.1 Healthcare Needs of the Ageing People Table 2.1 Mean time to walk four meters (Seconds) [5]

7

Age group Normal pace Rapid pace Number of participant 50–59

4.7

3.0

1,111

60–69

4.9

3.4

592

70–79

6.4

4.0

412

80 +

8.3

6.0

198

50–59 age group. The more senior group of ageing people tends to need more care and assistance in their daily life from the healthcare system, which will be discussed in the next section of this chapter. Beyond physical changes, ageing may also associate with changes in mental conditions due to life transitions such as retirement, house relocation, and even the death of close friends and family members. Thus, in developing a comprehensive healthcare system for the elderly, it is essential to look after the needs of their declining body functions and consider their psychological and social needs. Although medical technology has developed rapidly in the twenty-first century, there has been little change in the prevalence of limitations in functioning among ageing people. As pointed out by WHO, little evidence supports that the elderly today are in better health conditions than their parents [6]. The huge demand has made the elderly healthcare industry an enormous market with a fast-growing prospect. Report on China’s elderly care market (one of the sectors of China’s “Big Health” industry) revealed that the market value was about RMB5.6 billion (approximately USD1 billion) in 2017, which represented a 12% increment from the previous year, and was estimated to increase almost 20 times to reach more than RMB100 billion in 2030 [7]. However, it has been a global challenge for the community and government to develop an integrated healthcare system to meet ageing people’s specific demands and needs and their families. Generally, the elderly healthcare system could be categorised into a short-term care system and long-term care system, which contains three main components: hospital care services, home care services, and institutional care service.

2.2 Elderly Healthcare System As pointed out by WHO [6], the elderly healthcare system needs to be realigned to the needs of older people. The conditions and characteristics of the elderly are varied from young people. For example, the elderly often have multiple chronic diseases and face the condition of functional decline. Thus, the elderly healthcare system must provide the elderly-people-centric services and cares featuring their distinguished needs. For example, beyond the medical care for injury and other emergency care, the system must also focus on maintaining their functional capacities and responding to their psychosocial needs. To achieve an integrated elderly healthcare system, both short-term healthcare and long-term healthcare are needed. Figure 2.2 highlights the

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Fig. 2.2 Overview of the structure of elderly healthcare service system

structure of the elderly healthcare system discussed in this section.

2.2.1 Short-Term Healthcare Service Hospital care is the core component for the short-term healthcare service for all citizens, including ageing people. A hospital could provide different services, such as emergency medical care, trauma care, disease diagnosis and treatment, surgery. Thus, when the elderly become acutely ill, they usually look for emergency service or acute care in a hospital. In addition, the elderly tend to have more admissions to hospital, as well as staying longer than other age groups. Comparing with other age groups, the elderly use a disproportionate amount of hospital services [8]. Moreover, the usage pattern of hospital services of the elderly age group differed significantly from the younger age group. In Europe, the elderly aged 65 or above enter hospitals for various services such as “acute inpatient”, “inpatient complex continuing care”, and “inpatient rehabilitation” [9]. In addition, aligning with the health conditions of the elderly of tending to be more chronic and complex, the timely access and availability to hospital care services is also essential in preventing the fatal causes to the elderly. However, as the hospital system is usually established for the whole nation and not accommodated for the unique needs of the elderly, some problems may exist in-hospital care services. The unfamiliar environment in the hospital may make the elderly more challenging to adapt than younger adults. These challenges include hospital food or strange tastes leading to anorexia, getting burnt by hot packs, sensory loss of the passage of days and nights, etc. Furthermore, immobility in bed may cause problems like bedsores, and family separation may create mental problems.

2.2 Elderly Healthcare System

9

In the service industry, one of the approaches of problem-solving is through the improvement of service. Some hospitals now have special geriatric staffed with geriatric-trained nurses and physicians. With specialised equipment designed for the elderly, the geriatric-trained specialists manage to take care of the elderly by understanding their particular needs and addressing their requests. Besides, due to the limited healthcare resources in hospitals, i.e. staffing and facilities, the elderly may need to wait for a long time to receive treatments, leading to poor service satisfaction [10]. To relieve the workload and pressure on hospitals, this shift in promoting healthcare strategies from short-term care to long-term care is needed in the community [11]. Among the complete chain of healthcare services, elderly persons may require intermediate and long-term care services after being discharged from the hospital. They may be frail and need a professional caregiver to watch over or help them with their daily needs. Thus, the long-term healthcare service is crucial for the elderly, especially during their recovery.

2.2.2 Long-Term Healthcare Service Long-term healthcare service is an integral component of the elderly healthcare system. An effective long-term healthcare system can facilitate the release of the stress of the short-term healthcare system and reduce the financial burden of individuals and improve the quality of life for the elderly. The definition of long-term healthcare service varies among countries and sectors. In this chapter, the definition developed in a study report of WHO is adopted [12]. According to the definition, long-term healthcare services include activities undertaken for people requiring care by informal caregivers (e.g. family members and neighbours) and formal caregivers (e.g. health workers and social workers). In addition, the report also outlined that long-term healthcare was to ensure an individual without the full capability of taking long-term self-care “can maintain the best possible quality of life, with the greatest possible degree of independence, autonomy, participation, personal fulfilment, and human dignity”, and it may be home-base (i.e. home healthcare services) or institutional (i.e. institutional healthcare services). Specifically, long-term healthcare services including all kinds of personal care services (such as meal preparation, bathing, and laundry) and nursing services (such as convalescence, rehabilitation, and physiotherapy). As depicted in Table 2.2, statistics from Canada indicate that more than 70% of the elderly demand long-term healthcare services after discharge from a hospital. Keeping the elderly who are not critically ill at their home or in the community is one of the most cost-effective methods for the elderly healthcare system to take care of their health and fulfil their needs. Estimating indicated that a hospital bed’s daily cost would be serval times higher than a long-term care bed offered by an institutional health care centre and about 20 times higher than receiving healthcare services at home [13].

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Table 2.2 Discharge destination for elderly versus non-elderly (%) [13] Category

Discharge destination

%

% (Cumulative)

Long-term Healthcare Services

Long-term care

47

47

Non-long-term Healthcare Services

Rehabilitation

11

58

Home (with support services)

14

72

Home (without support services)

12

12

Died

12

24

Other

4

28

Home healthcare services Beyond cost-saving, the elderly would prefer home healthcare services just because they want to live in their home—as long as possible. Similar to the definition given by the National Clinical Homecare Association [14], the home healthcare service mentioned in this chapter refers to the provision of personal care services, medical supplies, and clinical services directly to the elderly in their homes. The scope of home care services could be preventive, rehabilitative, and palliative care services. Scope of home healthcare services. Home healthcare services are flexible and could be designed according to the actual conditions and specific requirements of the elderly to fulfil their individual healthcare needs. There is no agreed service list of home healthcare. Still, the main focuses of home healthcare services are: to provide personal care to the elderly to facilitate their daily life activities and maintain the quality of life, to perform regular checking and monitoring to achieve prevention and early detection of illness, to facilitate rehabilitation and physiotherapy after discharging from hospital, and to provide support and education to the family members and the community. Below are the critical elements of home healthcare [6]: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

Assessment, monitoring, and reassessment Health promotion & protection, disease prevention, postponement of disability Facilitation of self-care, self-help, mutual aid, and advocacy Health care, including medical and nursing care Personal care, e.g. grooming, bathing, meals Household assistance, e.g. cleaning, laundry, shopping Physical adaptation of the home to meet the needs of disabled individuals Referral and linkage to community resources Community-based rehabilitation Provision of assistive devices & equipment (e.g. hearing aids) and drugs Alternative therapies and traditional healing Specialised support (e.g. for incontinence, dementia, and substance abuse) Respite care (at home or in a group setting) Palliative care, e.g. management of pain and other symptoms Provision of information to the patient, family, and social networks Counselling and emotional support Facilitation of social interaction and development of informal networks

2.2 Elderly Healthcare System

18. 19. 20. 21. 22. 23.

11

Development of voluntary work and provision of volunteer opportunities to clients Productive activities and recreation Opportunities for physical activities Education and training of clients and informal and formal caregivers Support for caregivers before, during, and after periods of caregiving Preparation and mobilisation of society and the community for caring roles

Benefits of employing home healthcare services. Home healthcare is convenient for the elderly to receive care more cost-effectively. More importantly, the elderly desire to stay at home as it offers a comfortable environment for them and is near to their family and friends. According to a survey among ageing people in the United States conducted in 2012 [15], 9 out of 10 the elderly reported that they wanted to live in their current homes as they were getting older. The most selected reason for it was “I like my home and do not want to move out”, followed by “I have friends/family nearby” (Fig. 2.3). Some other benefits of home healthcare are listed in the following [16]. • Assists with specialised adaptations to your home environment to make your living space safer, more comfortable, and more accessible • Specialised services cater to the patient’s highly individual medical, personal, cultural, financial and emotional needs • Curtails frequency of hospital visits • Removes the need for hospital stays by providing high-calibre hospital-level care in an at-home setting • Patients can recover far more rapidly within comfortable home settings • Provides peace of mind to both the patient and their families • Helps to encourage visits from friends and family due to an easy visitation process.

Fig. 2.3 Reasons for elderly of planning to stay at home as grow older

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2 The Vision of the Healthcare Industry …

Institutional healthcare services Institutional healthcare is another composition for the long-term healthcare system. Institutional healthcare, or referred to as residential healthcare, is defined as healthcare provision to three or more unrelated people in the same place [11]. Elderly aged above 85 years old may be frail to receive care at home and tend to move to residential healthcare centres for the following reasons: their cognitive and physical functions were moderately or even severely impaired, they encountered difficulties in basic or instrumental activities of daily living, they lacked informal support, and caused significant burden and distress to the family or caregiver. Institutional healthcare centres can provide more valuable and demanding around-the-clock care to such elderly. Institutional healthcare services are usually provided by professionals (e.g. doctors, nurses, and social workers), auxiliaries (e.g. personal care workers helping with bathing and dressing), and traditional healers. In addition, neighbours, volunteers, and religious bodies may also provide informal care to the elderly in the institutional healthcare centre. Similar to home healthcare services, there is no agreed list of services or facilities that institutional healthcare must include. This industry is still under a rapid development stage, especially in developing countries (e.g., regulation). In the United States, the services provided by different institutional healthcare centres varied [17]. For example, in 2010, in the aspect of health care services (Fig. 2.4a), almost every institutional healthcare centre provides the service of “assistance with ADLs (activities of daily living)”, while “skilled nursing” service was only available in less than half of the centres. In term of services used by a resident, the highest percentage went to “basic health monitoring” (75%), followed by “assistance with ADLs (69%)”, which means that the demand for these two services was much higher than the others. On the other hand, for the supportive services (Fig. 2.4b), almost all the institutional healthcare centre offers services of “social/recreational activities in the residential care centre” and “personal laundry services” (which were also the most popular services that residents would use). At the same time, only around half of the centres provided “social services counselling” (which was also the least service being used by residents). It is easy to spot that the usage difference between social/recreational activities in and outside the residential care community was significant, this may imply that the social support function provided by the residential centre was practical although it was conducted within the centre. Take the institutional healthcare services in Hong Kong as an illustrated example of an institutional healthcare system. By estimation, the aged population in Hong Kong would increase significantly in the next few decades [18]. Table 2.3 shows that there will be significantly increments for all age groups above 65. For the more senior age groups (age group above 85 and age group above 100), the growth rates will be even faster than those of 65 or above in 2064. The great demand for institutional healthcare services is needed to be addressed due to the fast-growing population of the elderly. The Hong Kong government has established strategies to overcome the challenges of this issue, and one of the measures is to provide specific services and care to the elderly through the institutional healthcare system. As stated by the

2.2 Elderly Healthcare System

13

Fig. 2.4 a Institutional healthcare services in basic healthcare services. b Institutional healthcare services in supportive services Table 2.3 Population of the elderly in 2014, 2030 and 2064 [18] 2014

2030

2064

Age

Population Population Multiples of 2014 figure Population Multiples of 2014 figure

65+

1.065 M

2.106 M

2.0 times

2.582 M

2.4 times

85+

153,000

249,200

1.6 times

724,400

4.7 times

8,100

2.9 times

46,800

16.7 times

100+ 2,800

14

2 The Vision of the Healthcare Industry …

Table 2.4 Types and Features of Institutional Healthcare Services for Elders in Hong Kong [19] Type

Services

Applicant

Home for the Aged

Residential care, meals and a limited degree of assistance in activities of daily living

Unable to live No or mild independently in the community yet are not dependent on assistance with personal or nursing care

Impairment level *

Care and Attention Home for the Elderly

Residential care, meals, personal care and limited nursing care

Suffer from poor health Moderate or physical/mild mental disabilities with a deficiency in activities of daily living but are mentally suitable for communal living

Nursing Home

Residential care, meals, personal care, regular basic medical and nursing care, and social support

Suffer from poor health or physical/mental disabilities with a deficiency in activities of daily living but are mentally suitable for communal living

Severe

*Assessment under the ‘Standardised Care Need Assessment Mechanism for Elderly Services’

Social Welfare Department of the Hong Kong Government, institutional healthcare services in Hong Kong include Homes for the Aged, Care and Attention Homes for the Elderly, and Nursing Homes [19]. The aims of the services were to provide residential care and facilities for those unable to live at home to maintain their health and fulfil their personal, social, and recreational needs. The features of these three types of institutional healthcare services are described in Table 2.4 in detail.

2.3 Conclusion This chapter presented an overview of the ageing population and the existing elderly healthcare service system. By pointing out the structure of the elderly healthcare service system, two main types of elderly healthcare service systems, i.e. short-term healthcare service and long-term healthcare service, are highlighted. Key elements for those healthcare strategies are also identified to meet the specific needs of the elderly. Furthermore, it is feasible to adopt advanced technologies such as internetof-things (IoT), cloud computing, artificial intelligence (AI) and data mining techniques to facilitate decision making in allocating healthcare resources and healthcare management to deliver high-quality healthcare service to the elderly.

References

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References 1. World Health Organization. (2020). “Ageing”, in health at a glance: Asia/Pacific 2020: Measuring progress towards universal health coverage. OECD Publishing. 2. Atella, V., Piano Mortari, A., Kopinska, J., Belotti, F., Lapi, F., Cricelli, C., & Fontana, L. (2019). Trends in age-related disease burden and healthcare utilisation. Aging cell, 18(1), e12861. 3. World Health Organization. (2015a). World Report on Ageing and Health. Retrieved from https://www.who.int/ageing/events/world-report-2015-launch/en. 4. Besdine, R. W. (2019). Overview of aging. Retrieved from https://www.merckmanuals.com/ home/older-people%E2%80%99s-health-issues/the-aging-body/overview-of-aging. 5. World Health Organization. (2014). WHO Study on global ageing and adult health (SAGE) Wave 1. Retrieved from https://www.who.int/healthinfo/sage/en/. 6. World Health Organization. (2017). Nursing and midwifery in the history of the world health organization 1948–2017. Retrieved from https://ccoms.esenfc.pt/pub/2017_History_Nursingand-Midwifwery.pdf. 7. Asia Pacific Foundation of Canada. (2020). China’s evolving senior care sector. Retrieved from https://www.asiapacific.ca/sites/default/files/publication-pdf/china_senior_care_report_ final.pdf. 8. Kim, I., Song, H., Kim, H. J., Park, K. N., Kim, S. H., Oh, S. H., & Youn, C. S. (2020). Use of the National Early Warning Score for predicting in-hospital mortality in older adults admitted to the emergency department. Clinical and Experimental Emergency Medicine, 7(1), 61. 9. Eurostat Statistics Explained. (2018). Hospital discharges and length of stay statistics. Retrieved from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Hospital_dis charges_and_length_of_stay_statistics#Hospital_discharges_by_sex_and_age. 10. Naiker, U., FitzGerald, G., Dulhunty, J. M., & Rosemann, M. (2018). Time to wait: A systematic review of strategies that affect out-patient waiting times. Australian Health Review, 42(3), 286–293. 11. World Health Organization. (2018). Hospitals in the health system. Retrieved from http://www. who.int/hospitals/hospitals-in-the-health-system/en/. 12. World Health Organization. (2000). Home-based long-term care. Retrieved from https://apps. who.int/iris/bitstream/handle/10665/42343/WHO_TRS_898.pdf;jsessionid=88A57317F32C 215FD82D911ACEB7FDED?sequence=1. 13. Verbeeten, D., Philip A., & Gabriela P. (2015). Understanding health and social services for seniors in Canada. Ottawa: The Conference Board of Canada. Retrieved from https://silo.tips/ download/understanding-health-and-social-services-for-seniors-in-canada. 14. National Clinical Homecare Association. (2011). NCHA code of practice for clinical homecare service providers. Retrieved from https://www.clinicalhomecare.org/wp-content/uploads/ 2017/01/NCHA-Code-of-Practice_V4_approved_by_Board_160915ND.pdf. 15. UnitedHealthcare, USA Today, the National Council on Aging, & WPBT. (2012). The United States of Aging Survey—2012. Retrieved from https://www.aarp.org/content/dam/aarp/liv able-communities/old-learn/research/the-united-states-of-aging-survey-2012-aarp.pdf. 16. Aging In Place. (2020). All about home health care services. Retrieved from https://aginginpl ace.org/all-about-home-health-care-services/. 17. Khatutsky, G., Ormond, C., Wiener, J. M., Greene, A. M., Johnson, R., Jessup, E. A., Vree-land, E., Sengupta, M., Caffrey, C., & HarrisKojetin, L. (2016). Residential care communities and their residents in 2010: A national portrait. DHHS Publication No. 2016–1041. Retrieved from https://www.cdc.gov/nchs/data/nsrcf/nsrcf_chartbook.pdf. 18. Elderly Commission, HKSAR. (2017). Elderly services programme plan. Retrieved from https://www.elderlycommission.gov.hk/en/download/library/ESPP_Final_Report_Eng.pdf. 19. Social Welfare Department, HKSAR. (2018). Residential Care Services for Elders. Retrieved from https://www.swd.gov.hk/storage/asset/section/625/tc/Pamphlet_of_RCSE_ (May_2018)_final.pdf.

Chapter 3

Building Long-Term Care Services Around the World

3.1 Introduction Due to the increasing ageing population worldwide, there is an urgent need for governments and healthcare organisations to implement related policies and design healthcare systems to ensure the delivery and quality of healthcare services. Therefore, healthcare systems in different countries become a topic of interest to effectively utilise the healthcare resources and reduce the healthcare expenditure to satisfy the needs of the ageing population. In this chapter, healthcare policies in Taiwan, Hong Kong, Japan and the United States are studied.

3.2 Ageing Policies in Taiwan In Taiwan, a 10-yearlong-term care project called “Long-term Care Plan 1.0” was launched by the Ministry of Health and Welfare in 2007 for the target citizens aged 65 or above. It aimed to provide various care services, such as home nursing, nutritional meals, community rehabilitation, respite care services and long-term home care services for the elderly [1, 2]. Three goals are defined in the long-term care project as follows [3]. (i) (ii) (iii)

Deliver high-quality services; Improve the quality of life for the elderly through the enhancement of their independence; and Maintain the autonomy and dignity of the elderly.

In 2017, the government extended the coverage of the project by another ten years, i.e. Long-term Care Plan 2.0, to satisfy the continuously growing needs of long-term care services. Different to Long-term Care Plan 1.0, Long-term Care Plan 2.0 expands the goals of services [4] as follows: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_3

17

18

(i)

(ii)

(iii)

3 Building Long-Term Care Services Around the World

Establish a high quality, affordable and universal long-term care service system so that citizens with long-term needs can obtain essential services, enjoy their life in old age in a familiar environment, and reduce the care burden on families. Achieve ageing in place, provide multi-continuous services from supporting families, homes and communities to institutional care, as well as to popularise care service systems, and establish a caring community to improve the quality of life for those with long-term care needs. Extend the primary prevention function to promote the health and well-being of the elderly and improve their quality of life.

Seventeen kinds of services are covered in the Long-term Care Plan 2.0, as shown in Table 3.1. Also, the plan was expanded to cover the people with dementia aged 50 or above. A comprehensive community integrated care model with three tiers, namely the ABC network, was established to provide various services through hierarchical distribution planning [5]. Beginning with the long-term care management centre, care managers of the management centre assess the elderly needs, formulate a care plan and link with the stakeholders in the ABC network to implement the care plan for the elderly. The ABC network is the core innovation in the community-integrated care system of the Long-term Care Plan 2.0. Figure 3.1 shows the framework of the ABC network, which involves three tiers with different responsibilities. In Tier A, the care manager in the integrated community service centre determines benefits levels and care plans Table 3.1 Differences of the services provided between Long-term Care Plans 1.0 and 2.0 [6] Long-term Care Plan 1.0

Long-term Care Plan 2.0

Time

2007–2016

2017–2026

Targets

• • • •

Elderly aged over 65 Mountainous aboriginals aged 55 to 64 Disabled persons aged over 50 to 64 Elderly living alone and who need assistance according to the Instrumental Activities of Daily Living (IADL) scale

• People who are included in the 1.0 plan • People with dementia aged over 50 • Disabled lowland aboriginals aged between 55 and 64 • People aged under 49 with disabilities • Frail elderly

Care services Transportation shuttles Catering services Purchase of living aids, rent, and barrier-free living environment Home nursing care Home and community rehabilitation Respite services Long-term care facilities and services

• • • • •

Services • • • • • • • •

• • • • •

All who are included in plan 1.0 Dementia care services Community support services Family care support services Establish an integrated community service centre, a compound daycare centre, and alleyway long-term care stations Preventive care for disabilities Community preventive care network Health promotion Hospital discharge plan Home medical visits

3.2 Ageing Policies in Taiwan

19

Fig. 3.1 Framework of the ABC network [7]

and arranges the link care service resources according to the care plans to provide a localised delivery system that connects to the second and third layers. Tier B and Tier C are about combining service centre and long-term care (LTC) stations around the blocks, evaluating community capacity, and providing services in the neighbourhood. By doing so, the Long-term Care Plan 2.0 can allocate resources effectively and efficiently.

3.3 Ageing Policies in Hong Kong With the increasing life expectancy, it is challenging for the Hong Kong government to fully deal with the growing demands from the ageing population [8, 9]. In Hong Kong, the government policy on healthcare services is “ageing in place”, which causes an increased need for long-term care services in the form of community care services (CCS) and residential care services (RCS) [10]. It is estimated that the longterm care services will increase from nearly 60 000 places in 2016 to 78 000 places in 2030, and finally reach 125 000 places in 2051. To strengthen and promote the long-term care services, the Elderly Commission of Hong Kong launched the Elderly Services Programme Plan (ESPP) with three stages: scoping stage, formulation stage and consensus-building stage. Scoping stage: The scope of the ESPP is defined, and the key issues that need to be addressed are identified. Research related to the extensive environmental scan is also conducted to understand the existing situation of the elderly and services provided for the elderly [11]. Formulation stage: By analysing the identified critical issues in the scoping stage, a framework of ESPP is proposed, as shown in Fig. 3.2. By clearly defining the vision, mission, overarching principles and strategic directions, recommendations are provided, and the elderly care service delivery model is formulated, as shown in Fig. 3.3. Five major service components with different functions are defined as follows:

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3 Building Long-Term Care Services Around the World

Fig. 3.2 Vision, mission, overarching principles and strategic directions in ESPP

(i)

(ii) (iii)

Active ageing and community support—Provide the essential support to the elderly in maintaining their independence and productive life to achieve “ageing in place.” CCS—Enable the family of the elderly and foreign domestic helpers to provide care services to the elderly through the training programme RCS—Provide a wide range of institution services to the elderly to support their daily activities according to their needs, aspirations, affordability and other related factors

3.3 Ageing Policies in Hong Kong

21

Fig. 3.3 Elderly care service delivery model [12]

(iv)

(v)

Transitional Care—Provide temporary care services, including rehabilitation services and exceptional care support for the elderly who are newly discharged from hospitals to facilitate them to return to the community End of life care—Provide support such as life-and-death education to both the elderly and their family at the last stage of life of the elderly

Consensus building stage: Forums are organised in 18 Districts to introduce the ESPP for promoting long-term care services in the community. Long waiting time, misuse of emergency services, insufficient hospital beds and a lack of nursing staff are common problems that public hospitals may face due to the continuous increase of healthcare services. To address such issues, the Hong Kong government had put forward many measures to reduce the heavy burden on

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3 Building Long-Term Care Services Around the World

public hospitals. However, great attention and rejection were received from the public [13]. For example, the government significantly increased the service fees in public hospitals to reduce long waiting times and prevent misuse of emergency services. By doing so, the elderly with emergent needs can immediately use the appropriate services. To solve insufficient hospital beds in public hospitals, the government rented several beds from the private hospitals with lower service fees charged for receiving treatment at the private hospitals instead. On the other hand, for the elderly with chronic diseases, some community-based programs, such as The Elderly Health Care Voucher Scheme and The Elderly Vaccination Subsidy Scheme, was established to encourage the elderly to shift their essential care services required to the clinics [14]. A detailed assessment must tackle the upcoming ageing population challenges to maintain sustainable healthcare services in Hong Kong [15].

3.4 Ageing Policies in Japan Like Hong Kong, more than a quarter of the total population in Japan is aged 65 or above. In 2000, the Japanese government launched a long-term care insurance system [16]. The structure of the long-term care insurance system is shown in Fig. 3.4. Instead of providing personal care, the idea of this system is to support the independence of the elderly. Based on their needs, users, i.e. the elderly, can choose their services and services providers. Through the assessment, integrated medical and welfare services can be utilised. Two types of persons are insured under the long-term care insurance system, which is [17].

Fig. 3.4 Structure of the long-term care insurance system in Japan [18]

3.4 Ageing Policies in Japan

(i) (ii)

23

Primary insured persons: Persons aged 65 or above (32.02 million); and Secondary insured persons: Persons aged 40–64 covered by the health insurance system (42.47 million).

The beneficiary of needs and the corresponding quantity of services for the elderly can be determined by conducting the standardised survey. The survey may include a questionnaire and a report from professionals. To maintain the quality of life of the elderly, the Japanese government provided a wide range of integrated health and welfare services to the citizens in the community [19]. The basic concept of the overall care model of the community is to provide a comprehensive care system that integrates nursing, residential, medical, preventive and life support services within a limited duration [20]. In general, three types of services are included in the long-term care insurance system, which is: (i) services provided at home, (ii) services provided at care facilities, and (iii) locality-oriented services [21]. Services including outpatient rehabilitation, home-visit care, homevisit bathing care and in-home support care are covered in the services provided at home. Services provided at care facilities refer to the services delivered in the recognised insured care facilities, such as special nursing homes for the elderly, health services facilities for the aged and medical treatment beds for the long-term inpatients. Locality-oriented services refer to flexible services operated by individual municipal governments for enabling the elderly to live in their home communities [22]. Five levels are used to represent the health conditions of the elderly and the needs of the services required. Care facilities services are provided to the elderly with problems of levels 3–5, while home care services are provided to the elderly with problems of 1–2 [23]. Under clearcut rules in Japan, almost 90% of Japanese have a right to enjoy a specific amount of healthcare services from the long-term care insurance system.

3.5 Ageing Policies in the United States In 1971, the On-Lok Center in San Francisco’s Chinese community established the All-inclusive Care for Elderly (PACE) Program to provide comprehensive social and medical services to the elderly [24]. Due to the differences in languages and cultures, the traditional Chinese are hard to accept staying in nursing homes, and they prefer to receive the healthcare services at homes [25]. With the PACE, most participants can enjoy the comprehensive service packages and remain in the community rather than receive care services in the healthcare institutions [26]. People aged 55 or above living in the areas covered by PACE organisations are welcomed to join the PACE. They are eligible for receiving institutional care services and able to live independently in the community. The Californian government constructed an integrated community care model to provide a wide range of care services following the PACE. Such

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3 Building Long-Term Care Services Around the World

model included daily care, medical care, meal services, social services, pharmaceuticals, transportation and paramedical care for the elderly with a disability or chronic diseases [27]. With the increasing ageing population and the healthcare expenditure in the United States, the PACE currently also provides services to people with severe disability problems. Instead of living in nursing homes, it is believed that the elderly with minor health problems are happier to receive care services in the community, and it is also a benefit for them to maintain their quality of life [28]. In addition, with the shortage of healthcare resources and high operational costs in the healthcare institutions, integrated long-term care community services are a practical approach to properly allocate the resources to satisfy the needs of the elderly. The ultimate goals of PACE are to reduce the usage of healthcare institutions and maintain the quality of life of the disabled people in the community [29, 30]. Currently, 31 states in the US offer PACE plans to the elderly, enabling resource utilisation for service providers and enhancing longevity and quality of life for the elderly. Compared with non-PACE services, the states covered by the PACE have the lower utilisation rate of nursing homes, lower admission rate to hospitals and lower body function decline rate. Remarkably, with receiving good community care services, the number of visits to hospitals and readmission rate is effectively reduced, which implies that their health status is good and hence reduce the needs of admission to nursing institutions [31].

3.6 Conclusion Due to the rapid ageing phenomenon globally, long-term care service becomes increasingly important in providing various ongoing healthcare services to the elderly with disabilities or chronic diseases. To cope with the associated rising demands, governments/policymakers in different countries are concerned about establishing well-defined healthcare solutions for providing clear guidance for healthcare service providers to satisfy the needs of the elderly. In this chapter, the ageing policies in Taiwan, Hong Kong, Japan and the United States are reviewed. Establishing a comprehensive healthcare framework/model can facilitate coordination among various healthcare parties and allocate resources, i.e. staffing, equipment, and facilities, to deliver timely and accurate healthcare services. Considering only four counties have been reviewed in this chapter, future studies can be focused on comparing the characteristics of ageing policies worldwide. By doing so, valuable components/suggestions can be extracted and act as the reference for other countries to create their healthcare policies to address the problems raised by ageing issues.

References

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21. Nakanishi, M., Shimizu, S., Murai, T., & Yamaoka, A. (2015). “Ageing in Place” Policy in Japan: association between the development of an integrated community care system and the number of nursing home placements under the public long-term care insurance program among municipal governments. Ageing International, 40(3), 248–261. 22. Rhee, J. C., Done, N., & Anderson, G. F. (2015). Considering long-term care insurance for middle-income countries: Comparing South Korea with Japan and Germany. Health Policy, 119(10), 1319–1329. 23. Lin, H. R., Otsubo, T., Sasaki, N., & Imanaka, Y. (2016). The determinants of long-term care expenditure and their interactions. International Journal of Healthcare Management, 9(4), 269–279. 24. Reich, M. R., & Shibuya, K. (2015). The future of Japan’s health system—sustaining good health with equity at low cost. New England Journal of Medicine, 373(19), 1793–1797. 25. Veras, R. P., Caldas, C. P., Motta, L. B. D., Lima, K. C. D., Siqueira, R. C., Rodrigues, R. T. D. S. V., Santos, L. M. A. M., & Guerra, A. C. L. C. (2014). Integration and continuity of care in health care network models for frail older adults. Revista de saude publica, 48(2), 357–365. 26. Shaw, L. (2014). Program of All-Inclusive Care for the Elderly: A comprehensive, cost-effective alternative for frail elderly individuals. Long-Term Care, 75(5), 344–345. 27. Patel, K., & Rushefsky, M. E. (2014). Healthcare politics and policy in America. Public Integrity, 17(1), 94–96. 28. Fretwell, M. D., Old, J. S., Zwan, K., & Simhadri, K. (2015). The elderhaus program of allinclusive care for the elderly in north carolina: Improving functional outcomes and reducing cost of care: Preliminary data. Journal of the American Geriatrics Society, 63(3), 578–583. 29. Lehning, A. J. (2014). Local and regional governments and age-friendly communities: A case study of the San Francisco Bay Area. Journal of Aging & Social Policy, 26(1–2), 102–116. 30. Harris-Kojetin, L., Sengupta, M., Park-Lee, E., Valverde, R., Caffrey, C., Rome, V., & Lendon, J. (2016). Long-term care providers and services users in the United States: Data from the National Study of Long-Term Care Providers, 2013–2014. Vital & health statistics. Series 3. Analytical and epidemiological studies, 38, x–xii. 31. Gonzalez, L. (2017). A focus on the program of all-inclusive care for the elderly (PACE). Journal of aging & social policy, 29(5), 475–490.

Chapter 4

IOT and Cloud Computing for Development of Systems for Elderly and eHealth

4.1 Integration of IoT Technologies and Cloud Computing in Health Monitoring The global population is ageing due to the decrease in fertility and the increase in life expectancy. The World Health Organization (WHO) [1] estimated that the proportion of the world’s population over 60 years would nearly double from 12 to 22% between 2015 and 2050. In addition, WHO also stated that population ageing was much faster than in the past. In an ageing society, the healthcare of the elderly would become a critical social issue. In the meantime, the number of chronic diseases and the need for elderly long-term care are rising. However, the elderly want to remain independent and hope to stay in their homes for as long as possible [2]. The elderly tend to be more vulnerable to chronic diseases due to the decline in intrinsic capacity, and the demand for elderly healthcare is surging. While technology cannot slow down the ageing rate of the population, the development of IoT technology and cloud computing could make elderly healthcare more manageable, more accessible, and more affordable.

4.1.1 IoT Technologies and Cloud Computing in Health Monitoring The IoT technology, which can provide continuous and real-time information and data capturing without human intervention, is used to provide timely and integrated data to the formal caregivers (e.g., healthcare centre and hospital) and informal caregivers (e.g., family members and neighbours of the elderly). Generally, IoT technology consists of three layers: the device layer, the connectivity layer, and the application layer. The device layer contains various devices and sensors, e.g., wearable devices and wired/wireless environmental sensors, and relay nodes to collect © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_4

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data about personal conditions (such as skin temperature and heart rate) and the surrounding environment (such as humidity and temperature). The connectivity layer transmits data from the wearable devices and environmental sensors to the relay nodes via wireless communication technologies, e.g., Bluetooth and Wi-Fi. The relay nodes connect and transfer data to the concentrator via machine-to-machine (M2M) communication technologies, e.g., message queuing telemetry transport. IoT facilities such as cloud-based databases are applied to develop and manage various applications and systems in the application layer. The rapid growth of physiological sensors, low-power integrated circuits, wireless data transmission, and wireless communication leads to a creation of effective wearable devices for healthcare monitoring. These wearable devices could be integrated with personal accessories such as glasses, wristbands, headphones, and smartphones. Figure 4.1 demonstrates the applicable wearable devices network in elderly healthcare monitoring. Traditionally, timely monitoring and collecting the physiological information of aged people could be a significant challenge to the elderly caregivers, especially for those informal caregivers (e.g., family members, relatives, and friends). The creation of effective wearable devices and the application of IoT technology led to the real-time digital monitoring of physiological information. Which is, with the assist of wearable devices, personal information and data (e.g., by monitoring people’s body conditions and his/her activities) could be real-time monitored, collected, stored, and processed according to the needs of disease diagnoses and other specific requirements.

Fig. 4.1 Wearable devices network in elderly healthcare monitoring

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However, challenges also exist in the wearable device’s network, namely flexibility, scalability, and heterogeneous information in the IoT environments. With the integration of cloud computing, these challenges could be tackled with greater flexibility, scalability, and processing power in the IoT health monitoring. Cloud computing is a type of internet-based computing that provides shared computer processing resources and data to devices on-demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources which can be rapidly provisioned and released with minimal management [3]. In health monitoring, cloud computing provides users with a solution to access and apply a high-performance computing infrastructure, such as storage resources and computing applications on a pay-per-use basis, which would reduce the overall cost in elderly healthcare monitoring. With the vast applications, software, and services (e.g., software as a Service (SaaS)) provided in the cloud, data could be processed efficiently without limitation. In addition, cloud computing is more flexible and convenient for individuals to use anytime, anywhere with various devices. With the increase of data generated at the edge of the IoT network, edge computing has been introduced to improve data processing efficiency and real-time monitoring purposes [4]. Edge computing allows computation to be performed along the path between data sources and cloud data centres rather than the traditional approach to process data in cloud databases [5]. Figure 4.2 shows the two-way computing in edge computing. Fig. 4.2 Two-way computing in edge computing [4]

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4.1.2 Application of Remote Health Monitoring Remote health monitoring could be established with the application of IoT technologies and cloud computing. In this case, the elderly can reside in the comfort of their homes instead of taking the costly and limited healthcare services provided by healthcare centres or hospitals. Although the elderly are not physically staying in the healthcare centre or hospital, these formal healthcare service givers still can keep track of the elderly’s conditions and provide feedback or support to the elderly remotely. With wearable smart devices with medical sensors in elderly health monitoring, personalised and continuous healthcare can be provided at a relatively low cost for the elderly. On the one hand, continuous monitoring of the elderly could be achieved by caregivers. On the other hand, the caregivers can receive alerts triggered by the monitoring mechanism when detecting anomalies and take appropriate action timely. Take cardiac monitoring as a brief example. By utilising the wearable devices, the devices’ sensors will collect the acoustic parameters and other relevant details of the heart. After applying a suitable model to analyse the data, heart sound features would be analysed (e.g., classified into different features according to various needs and further processes), and abnormal heart activities could be detected and predicted. Figure 4.3 illustrates the elderly health monitoring mechanism structure, and the significant steps would be discussed as follows.

Fig. 4.3 Structure of the elderly health monitoring mechanism

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Data Collection. The wearable device’s network collects physiological biomarkers of individuals, such as temperature, respiratory rate, electrocardiography, electromyography. The devices would connect to the relay node, which is typically a smartphone located in the person’s vicinity, through wireless communication technologies, e.g., Bluetooth and Wi-Fi. Data Transmission. In this step, the data collected in the previous step would be conveyed to the concentrator, which is usually the data centre of the healthcare organisations, for data concentration and conducting further analysis. The collected data would be stored in the healthcare organisations, and in addition, the aggregated data would also transmit to the cloud to perform further data processing and analysis. Cloud Computing. The cloud is a crucial component in the mechanism. In this step, the cloud component would perform data storage, data analysis, and data visualisation. This component is designed for long-term storage of an individual’s biomedical information, perform in-depth data analysis, and provide diagnostic information to health professionals. Result Visualization. After the cloud computing step, the visualised results and diagnostic reports would be provided to both the individual and his/her appointed receivers, which are usually his/her doctors, family member or caregivers. This result would be helpful for the diagnoses and prognoses for some health problems and diseases. It should be pointed out that results provided after the analysis, rather than in an unprocessed format, are critical for elderly health monitoring. The results could educate the elderly and their family members. On the other hand, other relevant parties may take prompt action according to the results. In a word, wearable smart devices with medical sensors would be placed on the elderly to collect various information such as the dataset when they are standing, sitting, lying, walking, sleeping, and any other pre-defined information from the individuals. These well-defined and structured datasets would be transmitted to the concentrator and cloud platform for storage and analysis. The real-time information and suggestion would be provided to the elderly and their caregivers after performing computation via various machine learning techniques for health monitoring, disease diagnoses and prognoses, and risk prevention. In indoor environments, such enabling technologies can help detect falling by monitoring the elderly posture, and the details will be discussed in Chap. 7. Moreover, Swaroop et al. [6] proposed using the IoT technology to monitor the patients’ vital signs, including body temperature, blood pressure, and pulse. It can enhance communication between patients and clinicians and reduce the risk of losing track. In addition, Hung et al. [7] proposed a novel IoT-based positioning and shadowing system for training the short-term memory degeneration in dementia. With the application of IoT and cloud computing, different forms of monitoring and analysis such as heart rate monitoring, sleep quality analysis, and diabetic prediction could be conducted. In addition, there are some public datasets from patients monitoring such as PhysioNet, GitHub and World Health Organisation and data.

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4.1.3 Benefits of Remote Health Monitoring There are many advantages for healthcare providers and the elderly and their family members for taking remote health monitoring. Taking the elderly with diabetes as an example, as many changes may happen between their physician visits, instead of increasing the frequency of clinical visits of the elderly, remote monitoring could provide caregivers with continuous and instantaneous information while reducing their physical visits. In addition, the reduction of physical visits may also result in the reduction of inconvenience and risk during travel, especially for the dependent elderly. Generally, the benefits of remote health monitoring could be summarised as follows. Better life savings. The elderly are easier than the youth to suffer from chronic illnesses, such as hypertension, hyperglycemia or hyperlipidemia. Their lives would be dangerous when the blood pressure or blood glucose reaches a high level. With instantaneous measurement through remote health monitoring, the healthcare provider could receive alerts whenever an abnormal measurement is detected. With timely notification, the healthcare provider can take measures and intervene before the worst happens. Automation on data collection and fully data accessibility. With the application of wearable devices, routine health tests could be carried out automatically, and healthcare providers can get continuous and instantaneous information of the elderly for their analysis and decision making. In addition, healthcare providers could access the cloud anytime for the completed set of historical data. A sufficient and accurate historical data of the elderly is critical for doctors or caregivers to make professional judgements and diagnoses. Better engagement of both elderly and their family. The efficient and easy health monitoring could enhance the engagement of the elderly and their family member. Remote health monitoring improves elderly behaviour and comfort by creating a more engaging environment to be more accountable for their health. With the feeling of taking control of their health by the elderly, the quality of elderly healthcare could be enhanced simultaneously. Reduction on healthcare systems burden and cost on healthcare. On the one hand, with the application of remote health monitoring, the number of unnecessary physical visits to hospitals and medical institutions could be reduced. Thus, hospitals could become less crowded and can provide faster services to those needing emergency help. On the other hand, the cost of remote health monitoring is more affordable for most people, which provides an alternative to those who cannot afford to visit a doctor frequently. Educational support to the elderly. Besides providing continuous information to the appointed information receivers, formal caregivers (e.g., nurses and doctors) could also give professional feedback and support to the elderly and their family members through remote monitoring. Over time, the elderly and their family can get a lot

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of education which would be beneficial to the family member of the elderly in the long run, such as in observing abnormal in the elderly’s daily life and performing caregiving more skillfully.

4.2 Health Information Management System and eHealth With the wide adoption of remote health monitoring, a health information dataset consisting of the completed historical records of the elderly, such as physical changes, medication, and treatment outcomes, could be established. These valuable data can perform big data analysis for better data utilisation in diseases diagnoses and prevention. Originating from the need of analysing a large amount of data, big data was first introduced in 1997. Big data could be defined as a simple concept of vast sets of data [8]. Big data is believed to enable the development of new knowledge or value-adding decisions by uncovering the values of a vast amount of data [9], such as examining and exposing the hidden patterns, interesting relations, trends or any valuable information by various analytic techniques. It is also referred to as big data analytics. The analytic techniques could refer to statistical analysis, predictive analytics, and many data mining techniques and machine learning algorithms [8, 10]. However, challenges also exist. The challenges of big data analytics would be summarised as capture, storage, search, analysis, and virtualisation [11]. For example, there is a common problem, namely “information silos”, which needs to be solved before big data analytic techniques would be applied: as Wang and Alexander [11] pointed out, data fragmentation impedes the massive and timely exploitation of data. With the cloud technology discussed in the previous section, these challenges could be adequately tackled. The establishment of a Health Information Management System could also help pave the road.

4.2.1 Health Information System Initially, the health information system (HIS) could be traced back to 1928 when the American College of Surgeons established the Association of Record Librarians of North America. Then, its name was changed to American Association of Medical Record Librarians in 1938 and American Health Information Management Association (The Association) in 1991 [12]. The Association created the surgeon’s records from medical institutions, hospitals, medical experts, surveys, and librarians. In 1961, the Healthcare Information and Management Systems Society (HIMSS) created the information system with the information of workers activities, individual voluntary details, medical publications, and so on [13]. WHO [14] stated that “the health information system provides the underpinnings for decision-making and has four key functions: data generation, compilation, analysis and synthesis, and communication and use, and the health information system collects data from the health sector and other

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Table 4.1 Information for better decision making in healthcare [15] Information

Example

Health determinants

Data in socio-economic, environmental behavioural, genetic factors, etc

Health system and related processes

Health infrastructure, facilities and equipment, costs, human and financial resources, etc

Performance or outputs of the health system

Availability, accessibility, quality and use of health information and services, financial risk protection, etc

Health outcomes

Mortality, morbidity, disease outbreaks, health status, disability, wellbeing, etc

Health inequities

Coverage of services, health outcomes, key stratifiers such as sex, socio-economic status, ethnic group, geographic location etc

relevant sectors, analyses the data and ensures their overall quality, relevance and timeliness, and converts data into information for health-related decision-making”. According to WHO, health experts and decision-makers need various information, summarised in Table 4.1, and individual data collected from the health monitoring mechanism to make more precise decisions. As shown in Table 4.2, WHO [14] defined 29 core indicators for measuring HIS performance in six categories. For the category of “Capacity for analysis, synthesis and validation of health data”, which emphasised the capability of data collection, validation, and analysis, the remote health monitoring mechanism would be of great use. In the HIS, medical information and resources of a city or even a whole nation and health information of individual citizens would be gathered and integrated to achieve synergy effect in health monitoring and medical services provision. For example, the information of the elderly such as family history, habits, immunisation details, medication details, surgical history and so on would be recorded electronically in the HIS. With the application of HIS, clinical decisions would be made effectively in the medical field. In addition, with the adoption of big data analysis, HIS provides the ideal dataset for developing medical examination and further research analysis. Table 4.2 Categories for the measurement of HIS performance [14]

Category

Number of Core Indicators

Health surveys

5

Birth and death registration

3

Census

2

Health facility reporting

7

Health system resource tracking

4

Capacity for analysis, synthesis and validation of health data

8

Total

29

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35

Table 4.3 Measures for improving eHealth in WHO member states [15] 1. To consider drawing up a long-term strategic plan for developing and implementing eHealth services in the various areas of the health sector 2. To develop the infrastructure for ICT for health as deemed appropriate to promote equitable, affordable, and universal access to their benefits and to continue to work with partners to reduce costs and make eHealth successful 3. To build on closer collaboration with the private and non-profit sectors in ICT, to further public services for health and make use of the eHealth services of WHO and other health organisations, and to seek their support in the area of eHealth 4. To endeavour to reach communities, including vulnerable groups, with eHealth services appropriate to their needs 5. To mobilise multisectoral collaboration for determining evidence-based eHealth standards and norms, to evaluate eHealth activities, and to share the knowledge of cost-effective models 6. To establish national centres and networks of excellence for eHealth best practice, policy coordination, and technical support for healthcare delivery, service improvement, information to citizens, capacity building, and surveillance 7. To consider establishing and implementing national electronic public health information systems and to improve, using information, the capacity for surveillance of, and rapid response to, disease and public health emergencies

4.2.2 eHealth eHealth, also referred to as digital health, uses information and communication technologies (ICT) for health [16]. eHealth has been one of the prioritised issues for the WHO as the World Health Assembly resolution (i.e., WHA58.28) on eHealth was adopted in 2005: “eHealth is the cost-effective and secure use of ICT in support of health and health-related fields, including healthcare services, health surveillance, health literature, and health education, knowledge and research” [17]. As stated in WHA58.28, WHO urged member states to conduct a series of measures, listed in Table 4.3, to improve eHealth in respective countries. After recognising the potential of eHealth in 2005, WHO stressed the need for health data standardisation for eHealth and the importance of proper governance and operation of eHealth in 2013. The importance of ICT was also stressed. In 2018, WHO [1] further acknowledged the importance of digital technologies in the application of eHealth, and it urged members to “prioritise the development and greater use of digital technologies in health as a means of advancing the Sustainable Development Goals and promoting Universal Health Coverage in member countries.” Since recognising the importance of eHealth, the growth of eHealth has been accelerated in many countries. According to the published report of the global survey on eHealth in 2016 (WHO), 58% of the WHO Member States have an eHealth strategy, and 90% of the countries with eHealth strategy have special funding for eHealth development. In addition, more than 70% of the countries have institutions offering pre-service training or continuing education training on ICT for health professionals.

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The accomplishment of eHealth is needed support from a resilient health system. In this case, the HIS, with the integration of advanced ICT applications such as IoT technologies and cloud computing, could serve as a crucial component in eHealth infrastructure. Although it has been well recognised the crucial functions and roles of remote health monitoring and HIS in eHealth and Universal Health Coverage, some obstacles and challenges still need to be carefully addressed to utilise the advantages fully.

4.3 Challenges for Health Information System and eHealth The operation and potential benefits of remote health monitoring and HIS have been discussed in the previous sections. However, to achieve the practical application of HIS and eHealth, the following critical challenges are needed to be adequately tackled. Funding Issue Given the large number of various devices and the infrastructures requirements, and the complexity of remote health monitoring and HIS and eHealth, the initial investment and implementation cost would be high. Thus, enough funding may become a crucial barrier for the success of HIS and eHealth. In this case, the government may play an essential role in providing funding, making policy, and conducting surveillance on HIS and eHealth. Device issue. To meet the requirement of wearability, on the one hand, the sensors on the devices must be light, small, and should not impact the movements and mobility of the elderly. On the other hand, the batteries of the wearable devices also must be small and light and energy-efficient to reduce the need for charging. In addition, it needs to ensure that data will not lose during the battery charging and replacement period. Moreover, the accessibility of wearable devices and good broadband connectivity also needs to be carefully addressed, especially for the developing countries and some rural areas of a country. Security issue. Data security is a vital aspect of HIS and eHealth. Not only the elderly but also the healthcare organisations are afraid that private health data can be obtained by any unauthorised party and use for dubious purposes. Thus, it is essential to work out security solutions to guarantee data authenticity and confidentiality and safe data transmission to enhance the engagement of both the elderly and caregivers. For example, an authentication protocol between wearable devices and cloud servers should be well designed and established to secure communication and data transmission. Data issue. The data quality is essential for conducting further analysis, such as disease diagnoses and treatment decisions, which means the collected data must be accurate and reliable. However, errors in the data may occur and affect the reliability of the analysis and predictions. Some popular wearable devices appear to have significant variations of accuracy when collecting data from people. Thus, the lack of data

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reliability is a severe problem needed to be fixed before remote health monitoring, HIS, and eHealth could be fully utilised. Legal issues. This issue is requiring endeavour from the government level. Privacy protection and security of personal health data must be put in a high priority position and conducted at the country level, which means the country should have legislation to protect the data in HIS and eHealth. For example, a comprehensive legal framework of a nation may need to be established from data collection to data transfer and data utilisation between individuals and healthcare service providers.

4.4 Conclusion In this chapter, the infrastructure and applications of IoT and cloud computing in healthcare monitoring are discussed. It offers better life savings, automation on data collection and full data accessibility, better engagement of both elderly and their family and reduction on healthcare systems burden and cost. In addition, existing HIS and eHealth are also highlighted in this chapter for managing a large amount of health data. However, to achieve the practical application of HIS and eHealth, the user needs to address the challenges in data quality and privacy, high implementation cost, security of patient data. Therefore, future research can be focused on improving data security and privacy in handling a high volume of patient data.

References 1. World Health Organization. (2018). Hospitals in the health system. Retrieved from http://www. who.int/hospitals/hospitals-in-the-health-system/en/. 2. UnitedHealthcare, USA Today, the National Council on Aging, & WPBT. (2012). The united states of aging survey—2012. Retrieved from https://www.aarp.org/content/dam/aarp/livablecommunities/old-learn/research/the-united-states-of-aging-survey-2012-aarp.pdf. 3. Ramamoorthy, S. (2017). personalised health monitoring system using IOT and cloud. International Journal of Computer Science Trends and Technology (IJCST), 5. 4. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. 5. Wang, X., Han, Y., Leung, V. C., Niyato, D., Yan, X., & Chen, X. (2020). Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(2), 869–904. 6. Swaroop, K. N., Chandu, K., Gorrepotu, R., & Deb, S. (2019). A health monitoring system for vital signs using IoT. Internet of Things, 5, 116–129. 7. Hung, L. P., Huang, W., Shih, J. Y., & Liu, C. L. (2021). A Novel IoT based positioning and shadowing system for dementia training. International journal of environmental research and public health, 18(4), 1610. 8. Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319–330.

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9. Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101, 528–543. 10. Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2018). Big data analytics: Computational intelligence techniques and application areas. Technological Forecasting and Social Change, 119253. 11. Wang, L., & Alexander, C. A. (2015). Big data driven supply chain management and business administration. American Journal of Economics and Business Administration, 7(2), 60–67. 12. The American Health Information Management Association (AHIMA). (2021). History. Retrieved from https://www.ahima.org/who-we-are/about-us/history/#:~:text=The%20orga nization%20traces%20its%20origin,hospitals%20and%20other%20medical%20institutio ns.%22. 13. Healthcare Information and Management Systems Society (HIMSS). (2007). History of the healthcare information and management systems society. Retrieved from https://web.archive. org/web/20121203013925/http://www.himss.org/content/files/HIMSS_HISTORY.pdf. 14. World Health Organization. (2008). Health information systems. Retrieved from https://www. who.int/healthinfo/statistics/toolkit_hss/EN_PDF_Toolkit_HSS_InformationSystems.pdf. 15. O’Neill, K., Viswanathan, K., Celades, E., & Boerma, T. (2016). Monitoring, evaluation and review of national health policies, strategies and plans. Strategising National Health in the 21st Century: A Handbook. Geneva: World Health Organization, 1–39. 16. World Health Organization. (2021). eHealth at WHO. Retrieved from https://www.who.int/ehe alth/about/en/. 17. World Health Organization. (2005). eHealth. Retrieved from http://apps.who.int/iris/bitstr eam/handle/10665/20378/WHA58_28en.pdf;jsessionid=C0AE19271D0D0950EBBCC77B 3A5191CF?sequence=1.

Chapter 5

New Generation of Healthcare Services Based on Internet of Medical Things, Edge and Cloud Computing Infrastructures

5.1 Introduction Internet of Things (IoT) is among the most proliferative paradigms in the Cloud digital ecosystem. Various forms of IoT such as Industrial IoT, Internet of Cars, etc. One important application of IoT is in the health domain, where IoT is referred to as the Internet of Medical Things (IoMT). IoMT devices, either in embedded computing systems or as standalone devices, are more affordable each time due to the reducing cost of their products and being more reliable and accurate. Thus it is becoming easier to develop services and solutions in healthcare to improve patient’s quality of life (QoL) and Independent Assisted Living (IAL). Currently, developed countries spend a considerable amount of their GDP on healthcare [1]. Such investment in information technologies leads to improved quality for healthcare. It also leads to a significant reduction of the burden of expenses by avoiding hospitalization for patients whose diseases can be monitored remotely (see, for instance, WHO statistic reports [2]). The reduction of healthcare costs has become a must in all countries, particularly in developed countries where the ageing population is growing fast. The WHO statistics report [3] showed that the top 10 causes of death and cardiac diseases such as heart stroke are the most frequent causes of death globally. ICT aims to help alleviate and avoid such causes with the appropriate use of available technology. One particular field of health applications at large and healthcare systems for the elderly is that of applications based on the combination of three inter-related technologies: Internet of Medical Things (IoMT), Edge and Cloud Computing. Indeed, the fast development in Edge computing technologies leads to the implementation and decentralized deployment of healthcare systems at a large scale. Edge computing systems enable the push of intelligence to the edges of the Internet, breaking thus with the centralized view of intelligence based on Cloud computing. Thus, by processing

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_5

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the data close to end-users and data sources, it makes it possible to drastically reduce latency and round-trip time, enabling the implementation of real-life critical applications, such as those of healthcare for the elderly. However, integrating these new paradigms and related technologies poses many challenges at design and implementation levels and data integration, data privacy, reliability and real-time control, anomaly detection, etc. In this chapter, we focus, in particular, on the IoMT data stream processing, which has become the basis for much real-time monitoring and diagnosis in healthcare applications. Healthcare is a large producer and consumer of data: wearable devices attached to patients to measure and monitor dozens of vital parameters. Wearables can be worn or mated with human skin to continuously and closely monitor an individual’s activity without interrupting or limiting the user’s motion [3]. Several wearables per patient would easily lead to Big data and Big data streams. Healthcare data are characterized by the many V’s, among which the 4 V’s (Velocity, Volume, Variety and Veracity) are core properties of healthcare data. Statistical reports show that the number of wearables keeps pace by increasing year by year due to their low cost and various devices. The rest of the chapter is structured as follows. In Sect. 5.2, we overview the related work, Internet of Medical Things and other relevant technologies in the healthcare field. A generic type architecture of healthcare applications based on IoMT, Edge and Cloud Computing is presented in Sect. 5.3. Section 5.4 outlines the IoMT data stream processing for anomaly detection, focusing on heart diseases and ECG data sets.

5.2 Related Work, Internet of Medical Things and Stream Processing Engines The Internet of Medical Things has been the object of investigation of many recent studies in the interdisciplinary field of medicine, healthcare services, Internet of Things, data stream processing and analysis, etc. There are numerous studies published in the literature. Authors in [4–7] presented a remote patient monitoring system based on Electrocardiogram (ECG) data and accelerometers to trigger alerts to doctors and clinicians about elevated heart rate and detecting anomalous values that would lead to critical situations of patients.

5.2.1 Offloading Computation from Cloud to Edge Offloading computation from upper levels of Cloud computing towards edge devices represents many benefits to the Cloud digital ecosystem. Many authors have investigated how to achieve offloading efficiently and reliably. Different edge devices,

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41

such as Raspberry Pi, Arduino and embedded systems, are used for this purpose [8– 11]. Authors in [14] presented an edge-stream computing infrastructure for real-time analysis of wearable sensor data using Raspberry Pi for offloading computations.

5.2.2 The Intelligent Edge Nowadays, many authors are exploring the vision of the Intelligent Edge or the Edge Intelligence. That means the intelligent services are placed at edge and fog devices, capable of simple functions of data cleaning and filtering and more complex functions of anomaly detection, artificial intelligence, etc. Within such vision, variants of Intelligent Edge are analyzed in the context of healthcare applications. For instance, authors in [12] presented CareEdge: A Lightweight Edge Intelligence Framework for ECG-Based Heartbeat Detection. Similarly, the authors presented a system for IoT (Arduino’s electronic board) patient monitoring based on fog computing and data mining for the cardiac arrhythmia use case [13]. Authors in [14] and [15] presented an edge-stream computing infrastructure for the real-time analysis of wearable sensor data.

5.2.3 Design of Wearables Indeed, the proliferation of IoMT based applications in the Cloud digital ecosystem (also referred to as the Cloud-to-thing continuum) is propelled by the new generation of wearables for biomedical sensing and data acquisition. Such new wearables include textile wearables for contactless patient monitoring. These kinds of devices are making possible monitoring even for cases where patients are not willing to be monitored or are unwilling to collaborate with the monitoring system, as this is the case of patients in advanced stages of dementia. Thanks to continuous improvement in hardware design by considering medical, human, economic and environmental factors, devices with improved user-friendly interfaces are designed [16].

5.2.4 Semantic Edge The Semantic Web technologies have been a source of inspiration for using semantics in other fields of Internet-based applications. Among such fields, healthcare applications can benefit from semantic data enrichment and analysis by adding meanings to raw data collected by sensors. Typically, context information is added to sensor data creating thus a new stream of tuples enriched with more information. For instance,

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the authors evaluated IoT stream processing at the edge computing layer for semantic data enrichment [17]. The semantics concept has been in various works either for enrichment, processing or interoperability [18–21].

5.2.5 Stream Processing Engines Apache Kafka Apache Kafka is a publish-subscribe distributed messaging system that achieves a high throughput system thanks to its performance, scalability, and fault tolerance property. Kafka framework is useful for both stream and offline processing. Moreover, it can exchange data with relational databases, NoSQL databases or other distributed environments such as Apache Hadoop, Spark, Storm and Flink. Therefore, it can be said that Apache Kafka is one of the most critical messaging systems in the high-performance computing context, and in particular, for stream computing (with many TB of stored data and over 2 million writes per second). Apache Spark Apache Spark is a popular open-source framework for cluster computing of Big Data. It is distinguished for its high performance. Spark covers a wide range of workloads for batch and streaming applications, iterative algorithms and interactive queries. It offers a form of distributed shared memory that facilitates iterative algorithms and repeated data querying. It should be noted that Spark requires either a cluster manager like Hadoop YARN and a storage system, for example, HDFS or a NoSQL database. Apache Storm Apache Storm is an open-source real-time distributed system to handle Big Data streams used by large IT companies such as Twitter and Facebook. Storm provides a set of primitives for real-time computing. Storm uses a master-worker paradigm. Interestingly, even if the master node goes down, all workers can continue computation and produce outputs; the same observation is still valid if a worker goes down. Apache Flink Apache Flink is an open-source platform for distributed streaming and batch processing, specialized in practical real-time stream computing. Flink achieves high real-time throughput and very low latency during processing. Moreover, Flink also provides a batch processing engine to work on static data. Flink is stateful and faulttolerant with zero data loss; it can recover from failures while maintaining exactly one application state. Additionally, it performs large scale processing with thousands of nodes without affecting throughput and latency. A brief comparison among various features offered by these frameworks is summarized in the following Table 5.1.

5.3 A Generic Type Architecture of Healthcare Applications Based ...

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Table 5.1 A comparison among different frameworks Hadoop

Spark

Storm

Flink

Open source

✓

✓

✓

✓

Batech processing

✓

✓ ✓

✓

Trident

✓

Stream processing

Micro batching

Exactly once guarantees

✓

Latency

High

Medium

Very low

Low

Throughput

High

High

Low

Medium

Fault tolerant

✓

✓

✓

✓

Kafla supporting

✓

✓

✓

✓

5.3 A Generic Type Architecture of Healthcare Applications Based on IoMT, Edge and Cloud Computing Systems based on IoMT, Edge and Cloud Computing follow a layered architecture. Typically, the IoMT devices are at the lowest level (sensing level), and Cloud servers are at the highest level, where data is made persistent and deeply analyzed. Various applications, such as for the desktop, mobile, etc., can use such an architectural stack (see Fig. 5.1).

5.3.1 Sensing Layer The sensing layer provides the input data. Namely, the IoMT collected data from the entire system. This layer is the patient-closest layer, the source of data generation, and comprises IoMT wearables. Such wearables can employ different communication technologies, such as wired or wireless, to collect and send the data.

5.3.2 Pre-processing Layer The preprocessing layer is part of the edge devices layer, which retrieves data produced by wearables. Such edge devices could be Raspberry Pi, Arduino or other board units, including ones developed on purpose. In this layer, typical functions are data cleaning, error detection, missing data detection from the unbounded IoMT data stream coming from the sensing layer. More advanced functions include converting raw sensor data (priorly validated) to RDFStreams (a process of semantic data enrichment).

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Fig. 5.1 A generic layered architecture for IoMT data stream processing

In [14], the authors used a Raspberry Pi 3 as an embedded board, which hosts a Linux OS and a Node-Red server programming tool. The MQTT broker/Mosquitto nodes are used to get data coming from the sensing layer.

5.3.3 Cluster/Fog Processing Layer The Cluster/Fog processing layer comprises two (logical) clusters of commodity hardware or fog nodes, with specific computational resources capacity (beyond edge devices resource capacity). There are two tasks to be mapped to the cluster/fog nodes: Task 1: collecting semantically-enriched data, a kind of big data hub, to buffer data and be a safe dock to store temporary messages (data), which have to be analyzed. Task 2: running in real-time anomaly detection algorithms from streamed data and triggering critical situations or emergencies.

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There is an array of technologies to implement this logical mapping of tasks to cluster/fog nodes. One option would be to use Apache Kafka to perform the first task of collecting semantically enriched data, i.e., the Big Data Messaging Hub. It is scalable, fault-tolerant and guarantees high-performance computing. Kafka acts as a bridge between the preprocessing layer and the processing cluster and offers an access point for external systems which want to consume semantic data. The second task performs a stream processing of semantic-enriched data coming from the preprocessing layer and collected by the Kafka cluster. Different distributed stream processors which differ in performance and characteristics can be selected for this purpose. Three stream processors can be considered, namely, Apache Spark, Apache Storm and Apache Flink (see Sect. 5.2).

5.3.4 Persistence Layer The persistence layer is used to store data analyzed by the Cluster processing layer to allow full data analysis and an access point for external systems to retrieve stored data. Candidates for the persistence layer are NoSQL databases Cassandra and Apache HBase—they are column-oriented databases– to provide distributed storage. Still, they differ in their performance (Cassandra provides greater flexibility than HBase in terms of consistency control and the number of operations executed per second in load process context).

5.3.5 Application Layer Various applications can be built on top of this architecture. Such applications could be web-based, desktop or mobile. They can connect to the stream processing module, for instance, for anomaly detection such as patient data monitoring, transaction fraud detection, or can connect on top of the persistence layer for computing analytics, prediction, deep learning, etc.

5.4 IoMT Data Stream Processing for Anomaly Detection with Application to Heart Diseases and Real Data Sets Over the past decades, there have been various anomaly detection algorithms aiming to find anomalous patterns in the data, outliers, etc., understanding anomalous data as data that differs significantly from the majority of the data in the data set or the tuples of the data set the data stream. Typical anomaly detection algorithms are based on the

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statistical properties of the data stream. Recently, anomaly detection algorithms have attracted attention in networking, security, eHealth, among others. In healthcare, for instance, detecting anomalous vital signs has extreme importance to prevent sudden diseases and assure immediate medical intervention and assistance. More advanced anomaly detection algorithms have been developed based on unsupervised learning, supervised learning and semi-supervised learning.

5.4.1 Hierarchical Temporal Memory Algorithm (HTM) HTM is based on “neuroscience and the physiology and interaction of pyramidal neurons in the human brain’s neocortex”. HTM is inspired by the biological function of the neocortex and provides some algorithms based on continuous unsupervised learning, so it does not need training (see [22] for detailed description).

5.4.2 REALDISP Dataset This dataset is a fitness dataset for activity recognition [23, 24]. It contains several log files where wearable sensor values sampled at 50 Hz are recorded. Values come from different sensors on 17 individuals, and the whole dataset contains about 7 GB of data. Each record contains information about seconds and microseconds registered when data were collected and acceleration and orientation sensor values on three axes (x,y,z). Although this is a dataset at the disk, it can be used as input to the data stream processing architecture by reading the data in a stream mode, at a particular data rate, as coming from IoMT and feeding the system. The data rate in input can impact the whole processing chain, most notably, in the edge computing nodes (Raspberry Pi). Figure 5.2 is depicted the CPU and memory usage of the Raspberry Pi node (results are extracted from [25]). A full picture of the system performance can be seen in Fig. 5.3 for a multistream setting, corresponding to different numbers of sensors. The figure shows the cross-platform performance from Raspberry Pi to the persistence layer (Cassandra database). Finally, in Fig. 5.4, the HTM results for anomaly detection by processing the data set in the system showed high accuracy but dependent on the a priori set threshold value and when of using a network with a different resolution value.

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Fig. 5.2 CPU utilization and memory consumption on Raspberry Pi node

5.5 Conclusions and Prospects This chapter presents and discussed various challenges arising in healthcare applications and well-being for the elderly. The role of the Internet of Medical Things (IoMT), Edge and Cloud Computing Infrastructures for developing the new generation of healthcare systems for the elderly is analyzed, and its benefits are highlighted. In particular, we have analyzed the IoMT data stream processing, which has become the basis for real-time monitoring and diagnosis in healthcare applications. It is also beneficial for monitoring the elderly at home for ensuring QoL (Quality of Life) and AAL (Ambient Assisted Living). Besides QoL, the proposed systems and architecture can also positively impact the burden to hospitals, care centres, caregivers, and stakeholders. However, the development of such systems based on IoMT, Edge and Cloud Computing requires advanced computing infrastructures to ensure real-time reliability, security, privacy and security of patient data. IoMT data stream processing and anomaly detection open up many opportunities to improve the efficacy of the healthcare system for the elderly through timely detection and assistance.

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Fig. 5.3 CPU utilization and memory consumption on the layers of the system

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Fig. 5.4 Results on anomaly detection for different thresholds and resolution values

References 1. World Bank Group IBRD-IDA, Global Health Expenditure database, 2021 https://data.worldb ank.org/indicator/. 2. World Health Organization Statistics, 2021, http://www.who.int/mediacentre/factsheets/fs3 10/en/. 3. Gao, W., Emaminejad, S., Nyein, H. Y., Challa, S., Chen, K., & Peck A. et al. (2016). Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature, 529(7587), 509–14. 4. Chen, C.-M., Agrawal, H., Cochinwala, M., & Rosenblut, D. (2004). Stream query processing for Healthcare bio-sensor applications. 20th International Conference on Data Engineering, 2004, IEEE. 5. Cosoli, G., Spinsante, S., Scardulla, F., D’Acquisto, L., & Scalise, L. (2021). Wireless ECG and cardiac monitoring systems: State of the art, available commercial devices and useful electronic components. Measurement, 177. https://doi.org/10.1016/j.measurement.2021.109243. 6. Lv, W., & Guo, J. (2021). Real-time ECG signal acquisition and monitoring for sports competition process oriented to the Internet of Things, Measurement, Volume 169. https://doi.org/10. 1016/j.measurement.2020.108359. 7. Manisha, Dhull, S. K., & Singh, K. K. (2020). ECG beat classifiers: a journey from ANN To DNN. Procedia Computer Science, 167, 747–759. https://doi.org/10.1016/j.procs.2020. 03.340. 8. Yakut, O., Solak, S., & Bolat, E. D. (2014). Measuring ECG Signal using e-health sensor platform, international conference on chemistry, biomedical and environment engineering (ICCBEE’14). 9. Magana-Espinoza, P., Aquino-Santos, R., C˜ ardenas-Benitez, N., Aguilar- Velasco, J., Buenrostro-Segura, C., & Edwards-Block, A. et al. (2014). WiSPH: A wireless sensor network-based home care monitoring system. Sensors, 14(4), 7096–7119. 10. Orha, I., & Oniga, S. (2013). Automated system for evaluating health status, 2013. IEEE 19th International Symposium for Design and technology in Electronic Packaging (SIITME), pp. 219–222.

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11. Bora, P., Kanakaraja, P., Chiranjeevi, B., Jyothi Sri Sai, M., & Jeswanth, A. (2021). Smart real time health monitoring system using Arduino and Raspberry Pi, Materials Today: Proceedings, 2021, https://doi.org/10.1016/j.matpr.2021.02.290. 12. Zhen, P., Han, Y., Dong, A., Yu., & Jiguo. (2021). CareEdge: A lightweight edge intelligence framework for ECG-based heartbeat detection. Procedia Computer Science, 187, 329–334. https://doi.org/10.1016/j.procs.2021.04.070 13. Moghadas, E., Rezazadeh, J., & Farahbakhsh, R. (2020). An IoT patient monitoring based on fog computing and data mining: Cardiac arrhythmia use case. Internet of Things, 11. https:// doi.org/10.1016/j.iot.2020.100251. 14. Ritrovato, P., Xhafa, F., & Giordano, A. (2018). Edge and cluster computing as enabling infrastructure for internet of medical things. AINA 2018, pp. 717–723. 15. Greco, L., Ritrovato, P., & Xhafa, F. (2019). An edge-stream computing infrastructure for real-time analysis of wearable sensors data. Future Gener. Computer System, 93, 515–528. 16. Krishnan, S. (2021). 2—Wearables design, Editor(s): Sri Krishnan, Biomedical signal analysis for connected healthcare. Academic Press, pp. 31–84, https://doi.org/10.1016/B978-0-12-813 086-5.00002-5. 17. Xhafa, F., Kilic, B., & Krause, P. (2020). Evaluation of IoT stream processing at edge computing layer for semantic data enrichment. Future General Computer System, 105, 730–736. 18. Gonçalves, B., & Guizzardi, G. (2011). José G. Pereira Filho, Using an ECG reference ontology for semantic interoperability of ECG data, Journal of Biomedical Informatics, 44(1), 126–136. https://doi.org/10.1016/j.jbi.2010.08.007 19. Londhe, A. N., & Atulkar, M. (2021). Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM. Biomedical Signal Processing and Control, 63. https://doi.org/10.1016/j.bspc.2020.102162. 20. Lima, V. C., Alves, D., Pellison, F. C., Yoshiura, V. T., Crepaldi, N. Y., & Lopes Rijo, R. P. C. (2018). Establishment of access levels for health sensitive data exchange through semantic web. Procedia Computer Science, 138, 191–196.https://doi.org/10.1016/j.procs.2018.10.027. 21. Wu, H., Toti, G., Morley, K. I., Ibrahim, Z., Folarin, A., Kartoglu, I., Jackson, R., Agrawal, A., Stringer, C., Gale, D., Gorrell, G. M., Roberts, A., Broadbent, M., Stewart, R., & Dobson, R. J. B. (2017). SemEHR: surfacing semantic data from clinical notes in electronic health records for tailored care, trial recruitment, and clinical research. The Lancet, 390(3), S97, https://doi. org/10.1016/S0140-6736(17)33032-5. 22. Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134–147. https://doi.org/10.1016/j.neucom.2017. 04.070. 23. Baños, O., Tóth, M. A., Damas, M., Pomares, H., & Rojas, I. (2014). Dealing with the effects of sensor displacement in wearable activity recognition. Sensors, 14(6), MDPI AG. 24. Baños, O., Damas, M., Pomares, H., Rojas, I., Tóth, M. A., & Amft, O. (2012). A benchmark dataset to evaluate sensor displacement in activity recognition. ACM Conference on Ubiquitous Computing, 2012, ACM. 25. Giordano, A. (2017). Semantic stream computing for large dataset analytics. Master Thesis, 2017, Faculty of Informatics of Barcelona, Universitat Politècnica de Catalunya, Spain (Supervised by Prof. Pierluigi Ritrovato and Prof. Fa.

Chapter 6

Artificial Intelligence and Data Mining Techniques for the Well-Being of Elderly

6.1 Introduction With the increasing concern of the ageing population worldwide, the amount of health data is dramatically increased in terms of volume, velocity, variety, integrity, and value. To extract useful information from health data, the adoption of artificial intelligence (AI) and data mining in healthcare is growing while radically changing healthcare delivery. There are many stakeholders among the healthcare supply chain, such as healthcare staff, the elderly, healthcare services providers and organisations. Data mining and AI techniques provide data insights for stakeholders to uncover hidden connections and patterns and provide the knowledge for decision making to mitigate health risks and harm. In this chapter, three AI techniques, including fuzzy logic, case-based reasoning, genetic algorithm and analytic hierarchy process, are reviewed. In comparison, three data mining techniques, including clustering, classification and association rule mining, are studied.

6.2 Artificial Intelligence Techniques Artificial intelligence (AI) is defined as a system’s capability to properly understand external data, absorb from such data, and use those learnings to do particular goals and tasks through flexible reworking [1]. The concept of AI was evolved in the 1940s–50s when scientists began to discuss the possibility of creating an artificial brain. AI research was financially supported and defined at the Dartmouth Conference in 1956 [2]. Since then, some logic, reasoning methods and models were explored and applied in different areas like computer science, robotics and statistics. The following explains various common AI methodologies and techniques that help machines interpret data and achieve particular tasks in the healthcare industry.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_6

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6.2.1 Fuzzy Logic Fuzzy logic is a method of reasoning that simulate human thinking [3]. It extended Boolean logic based on the mathematical theory of fuzzy sets. The rules are set in natural language “yes” or “no”, which is equivalent to computer language “true” or “false”. It can be implemented in workstation-based control systems, which can control hardware and software. A general fuzzy system has four components: fuzzification, fuzzy rule base, fuzzy output engine, and defuzzification [4]. Figure 6.1 below shows the modelling process of a general fuzzy system. In fuzzification, it would convert each input data into a degree of membership functions. The membership function would take any value between 0 and reflect the degree of membership [5]. It splits the input data into several measurement scales for the determination afterwards. Fuzzy base rules are the rules that include all possible fuzzy relations between inputs and outputs. There are no mathematical equations in a fuzzy approach, and the uncertainties, nonlinear relationships, and model complications are included in the form of If–Then statements. The fuzzy output engine is the decision-making unit at the module. It considers all the fuzzy rules in the fuzzy rule base and learns how to transform a set of inputs to corresponding outputs. Defuzzification converts the resulting output from the fuzzy output engine to a number. There are numerous defuzzification methods such as the centre of gravity (COG), the bisector of area (BOA), mean of maxima (MOM), leftmost maximum (LM), and rightmost maximum (RM) [6]. In application, fuzzy logic can be applied in systems such as traffic control systems, intelligent highway systems, air conditioning systems, microwave ovens, production optimisation, and even stock market prediction systems. In the healthcare industry, Medjahed et al. [7] applied the fuzzy logic in nursing homes for monitoring and predicting the daily activities of the elderly. Hussain et al. [8] developed a home healthcare system using fuzzy logic to monitor the health status of patients with heart diseases. Parameters such as body temperature, heart rate, respiration rate and blood oxygen level are treated as the input for the fuzzy system for providing timely

Fig. 6.1 Process of a fuzzy logic system

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treatments. The Multi-layer of the fuzzy logic system is designed to convert multiobjective problems to single output for providing the recommendation to handle various dimensions in fuzzy logic [9]. Figure 6.2 shows the example of the multi-layer of fuzzy system.

Fig. 6.2 Fuzzy model with multi-layers [9]

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6.2.2 Case-Based Reasoning Case-based reasoning (CBR) is a method that is solving new problems based on the solutions of similar past problems. Roger Schank introduces the basic idea of case-based reasoning in the early 1980s. At the same time, many similar theories occur, such as legal reasoning and memory-based reasoning, which is a combination of CBR with another reasoning method. The interest in CBR raises globally in the 1990s. An international conference on CBR was conducted to standardise the CBR in 1995. Most of the current CBR systems, they usually designed to assist designers in solving a designed problem [10]. The general CBR model has been formalised for computer reasoning as a four-step process, as shown in Fig. 6.3. They are. (i) (ii) (iii) (iv)

Case retrieval: retrieve the most similar case; Case reuse: reuse the retrieved case for solving the new problem; Case revision: revise the content in the solutions if necessary; and Case retention: retain the solutions as the new case stored in the case library.

There is a database of the case (case library) in the CBR system; it is responsible for storing all the cases. The record stored in the case library is divided into the problem part and solution part in the database [11]. The problem part includes relevant case Fig. 6.3 Four steps in CBR process

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features, and the solution part mentions handling the conclusion and comments. At the retrieve process, the CBR engine would search for potentially valuable cases in the case library. A group of potential claims is retrieved by matching a new problem using the k-d tree indexing method. The group of cases would present for case reuse and enter the following process. With a group of potential cases generated in the previous step, similarity values of potential cases can be calculated by the nearest neighbour method, as shown in Eq. (6.1). The value is calculated by the sum of all attributes with weightings. Where wi represent the weights added to f i . Then the cases are ranked in descending order by similarity value. n i=1

  wi sim f i1 , f i R n i=1 wi

(6.1)

At the stage of revision, the CBR engine suggests a historical case; it requires the user to make modifications. Such as combining and editing the details inside the case to generate a new solution to the problem input. This step ensures the final case suggestion matches the actual situation and increases the success rate after adoption. In this stage, the ultimate solution is generated and leave a record for the next step. CBR engine would store the record at retain process. The revised report is sent to the case library for storage. Cases in the case library are stored for future use, which would be used in the next round of the cyclical process. After the CBR engine has been applied for some time, the maturity of the whole system would increase. The applications of CBR provides benefits for healthcare organisations in planning resources, improving the staff services quality and diagnosing diseases [12, 13]. For example, Wang [14] developed a healthcare system using CBR to formulate treatment plans for patients with mental diseases effectively. Petrovic et al. [15] applied the CBR to facilitate the decision making in generating the radiotherapy treatment planning. Choy et al. proposed an intelligent case-based knowledge management system using fuzzy logic and CBR to assess the quality of nursing staff during the services delivery and formulate a re-training program for quality improvement [16].

6.2.3 Genetic Algorithm Genetic Algorithm (GA) provides a searching function based on the natural selection mechanism and natural genetics [17]. It contains an iterative process to preserve population structures that are the possible solution for the challenges in a specific domain. In each generation, the structure in the present population will be ranked according to the effectiveness of the problem. In light of evaluations, a new population structure will be established by using genetic operators. Figures 6.4 and. 6.5 AHP hierarchy shows the framework of GA.

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Fig. 6.4 Framework for GA

GA usually operates in a situation of a population that is created by a group of randomly created individuals [18]. The individuals inside the population will be evaluated. The evaluation system will score the individuals according to their performance [19]. Two individuals with the highest scores would be picked out for creating one or more offspring. This process will repeat until an acceptable solution is formulated or meet the requirement of the program designer. Throughout this process, two genetic operators are commonly used: mutation and crossover [20]. Crossover is a genetic operator used for dividing the programming of chromosomes from one generation to the next one [21]. Crossover is a process that reaches a child solution from multiple parent solutions. Several crossover techniques are commonly used, including single-point crossover, two-point crossover, uniform crossover, half uniform crossover and three parent crossover, and different techniques in different data structures.

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Fig. 6.5 AHP hierarchy

On the other hand, the mutation is a genetic operator used to retain the genetic diversification of a chromosome population in the genetic algorithm [22]. Like the biological mutation, it would transform one or more gene values from its initial state into a chromosome. In the mutation used in the genetic algorithm, the resulting solution may be different from the initial solution, and therefore, it may become a better solution. The main focus of mutation in a genetic algorithm (GA) is diversity. Local minima should be averted by forbidding the population of a chromosome to be similar. Moreover, more GA systems add some random weighting selection onto the fittest of the population to avoid similar chromosomes slowing down the evolution [23]. Because of the benefits offered by GA, Hertz and Lahrichi [24] adopted the GA in home care services for scheduling healthcare staff. In their study, factors including the geographic location of the patients and the workload of nurses are considered. These factors are important because they may affect the quality of the service. The GA operations help provide the optimal solutions for the home care centre to reduce travel time in visiting patients. Bekker et al. [25] developed an intelligent system using GA and linear programming to schedule tasks in the nursing home. By matching the care tasks to the appropriate care workers, healthcare resources can be better allocated. Furthermore, GA can be used for medical diagnosis. Anbarasi et al. [26] applied GA to enhance the accuracy and reduce the time in predicting heart diseases. Ye [27] introduced a novel hybrid approach that integrates GA and swarm optimisation algorithms for improving the classification performance in medical diagnosis.

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6.2.4 Analytic Hierarchy Process (AHP) The analytic hierarchy process (AHP) is a designed technique for establishing and analysing intricate choices based on mathematics and psychology. In AHP, preferences between options are determined by making pairwise judgements, in which two alternatives are compared under one criterion. As shown in The AHP has the following steps: 1.

2.

3.

Problem structuring: The problem is structured according to a hierarchy where the highest element is the goal of the decision. The next level of the hierarchy denotes the criteria, and the lowest level represents the options. In more complex hierarchies, more levels can be added. In any case, there is a minimum of three levels in the hierarchy [28]. Priority calculation: A priority is a score that positions the importance of the alternative or criterion in the judgement. It is derived from the numerical answers in each pairwise comparison. The criteria (level 2) are pairwise compared against the goal for importance (level 1). The alternatives (level 3) are pairwise compared against each of the criteria for preference (level 2). The comparisons are handled mathematically, and priorities are derived for each node [29]. Decision-making: based on the calculated scores of the alternatives, the alternative with the highest score is preferred.

AHP is effective in solving complex problems that involve quantitative and qualitative factors [30]. Traditional AHP is based on a linear scale and subjective judgement on the preference scales, leading to imprecise judgments. Research has shown the hybrid AHP approach to eliminate the drawbacks of traditional AHP [31]. The integration of QFD, AHP and fuzzy logic is widely applied in quality management and logistics [32]. In healthcare aspects, AHP is mainly used to select the healthcare services and analyse service performance. Azam et al. [33] designed fuzzy AHP to select the healthcare establishment quality to improve resource allocations in healthcare organisations. Singh and Prasher [34] integrates fuzzy concepts and AHP to measure the healthcare service quality in hospitals considering dimensions including tangibles, responsiveness, reliability, assurance, empathy and trustworthiness.

6.3 Data Mining Techniques In this Information age, an enormous amount of data are gathered every day for business and society to perform data analysis to discover new knowledge [35]. To facilitate the transfer of extensively growing data into useful knowledge, powerful and functional devices or data mining methods are necessary. The need has led to the emergence of data mining. Brown [36] addresses the prevalence of using data mining principles to process data and recognise trends. He adds that the onset of big

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data has paved the way for people to utilise wide ranges of data mining methods. There are many data mining techniques, such as classification, time series analysis, association rules, clustering and regression [37]. Data mining techniques have become more important in processing the evergrowing size of information. Obenshain [38] and Srinivas et al. [39] suggest that data mining techniques can improve service quality in the healthcare sector by extracting hidden patterns in data. Candelieri et al. [40] agree that data mining tools favour and enhance the decision-making processes since they can help reduce human errors like misjudgement or subjectivity. While more information is needed to be classified in the healthcare industry nowadays, data mining has become more significant. The widespread information technology has enabled healthcare service providers to manage medical knowledge safely and comprehensively; medical records can be retrieved and restructured as meaningful knowledge, which could reduce medical costs [41]. More accurate clinical decisions can be made to enhance the treatment quality for patients. Additionally, the previously unconnected information in the database can be related. The undiscovered trends from historical data can be found in the data mining process, which can be turned into critical business solutions [42].

6.3.1 Clustering Clustering is a process of grouping data; the objects within the same cluster carry the same property. The general clustering process are (1) collecting data, (2) clustering by hierarchical clustering or partitional clustering, (3) testing the clustering result, and (4) evaluate the clustering result and go back to step 2 if the goal is not met. There are two main types of clustering, hierarchical clustering and partitional clustering. Hierarchical clustering is a clustering method that aims to build a hierarchy of clusters. There are two general approaches in hierarchical clustering which is the agglomerative and divisive approach. On the other hand, partitional clustering is a clustering method that aims to group data with similar characteristics. Also, k-means clustering can organise unlabeled data objects into groups based on similarity [43]. K-means clustering is one of the partitional clustering techniques. It classifies data into k groups of data. Each group must carry at least one data object, and each data should belong to only one group. Then, data were partitioned into k clusters based on the mean value in each cluster. Finally, data were classified into k groups. The following shows a detailed step in the k-means algorithm. 1. 2.

Selects k number of objects randomly. Each value represents a mean of one cluster. Calculate the differences between each of the remaining objects and means base on Eq. (6.2). k  n  2   ( j)  J= xi − c j  j=1 i=1

(6.2)

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Table 6.1 K-means algorithm example xi

Distance A

Distance B

Cluster

New mean

110

14

23

A

115.6

124

0

9

A

115

9

18

A

109

15

24

A

120

4

13

A

140

16

7

B

133

9

0

B

144

20

11

B

135

11

2

B

138

( j)

3. 4. 5.

where k represent the number of clusters, n represent the number of cases, xi represent case i, and cj represent cluster j. Assign the remaining data to a cluster which have the shortest distance. Compute a new mean within a cluster. Repeat steps 2, 3, and 4 until there are no further changes.

Example Suppose there we want to group 9 students into 2 clusters using their heights (in cm) as follows: n = 9, k = 2, {110, 140, 133, 124, 115, 109, 144, 135, 120} First, set the centroid by random c1 = 124, c2 = 133. Next, find out the distance between each mean and each data, as shown in Table 6.1. Then, five students were classified in class A, and four students were classified in class B. After that, new means can be found, which are 115.6 and 138. Then, repeat the steps until no further change. Finally, the result of the k-mean clustering can be calculated, as shown in Table 6.2. The clustering technique is widely used in the healthcare industry. Belciug et al. [44] propose to use a clustering-based method to recognise the repetition of breast cancer. Escudero et al. [45] use k-means clustering to analyse the patterns of Alzheimer’s Disease (AD) to facilitate early detection of the disease. With the help of the k-means clustering technique, Balasubramanian and Umarani [46] reveal the potential risks to human health when consuming water with fluoride. Vithyaa and Manivannan [47] adopted the parallel k-means clustering algorithm to analyse the performance of healthcare applications.

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Table 6.2 K-mean clustering results xi

Distance B

Cluster

New mean

110

Distance A 5.6

28

A

115.6

124

8.4

14

A

115

0.6

23

A

109

6.6

29

A

120

4.4

18

A

140

24.4

2

B

133

17.4

5

B

144

28.4

6

B

135

19.4

3

B

138

6.3.2 Classification Tomar and Agarwal [48] describe the classification technique as a ‘supervised learning method’ in which sample data are sorted into different target classes. Several commonly used classification algorithms are adopted in the healthcare industry, such as the k-nearest neighbour (kNN) and decision tree. A k-nearest neighbour (kNN) classification, a multi-dimensional space in which every dimension correlates to a specific trait, is built [49]. The space is full of data points with a particular trait so that classification can be established by the most significant part of the k-nearest neighbour [50]. An example of the kNN classification is presented in Fig. 6.6. The decision tree provides a graphical tree-like diagram that shows the hidden relationship within the data in the database. By sorting down the instances from the root node to the leaf nodes down the tree, they can be classified and understood based on the if–then condition [51]. Sahan et al. [52] suggest a mixed-use of kNN classification with an artificial fuzzy system to detect the pattern of breast cancer. Anunciaçao et al. [53] give proof of

Fig. 6.6 An example of the kNN classification [50]

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the usefulness of decision trees for detecting the group of people who has a high risk of breast cancer. Recently, hybrid approaches are proposed to predict breast cancer. Pawlovsky and Matsuhashi [54] embedded the GA in the kNN operation for improving the accuracy of component selection in breast cancer diagnosis. In Gunawan et al. [55], they considered ten physical parameters such as entropy, contract and angular second moment with the kNN method to detect the level of breast cancer to enhance their reliability.

6.3.3 Association Rule Mining Tomar and Agarwal [48] suggest that frequent patterns and useful relationships in a dataset are often found using the imperative approach- Association. Lee and Cheung [56] claim that the co-occurrence of features in a given database can be discovered and converted into valuable information using association rules. Apriori algorithm is the most classical and influential data mining algorithm in the association rules mining proposed initially by Agrawal et al. [57]. The algorithm can extract and generate k-itemset(s), which are frequently seen in the database. An example of the apriori algorithm with a 1-itemset is shown in Fig. 6.7. Two inputs, which are named support and confidence, are required in the Apriori algorithm. Support represents the frequency of occurrence in a given database; Confidence represents the accuracy of the occurrence [58]. The concept behind this algorithm is to improve the efficiency of data mining by cutting down the cardinal number of possible itemsets in every cycle of the calculation [59]. However, association rules mining is limited to binary transaction data only, which indicates whether the product is bought or not. In real life, data are more dynamic, which can be quantitative or multivalued. Thus, the fuzzy association rule mining approach is proposed [60]. Fig. 6.7 An Apriori algorithm with a 1-itemset

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In the healthcare industry, association rules are widely used to enhance disease prediction and enhance the quality of medical treatment. In Karabatak and Ince [61], an intelligent system that utilises association rules and neural networks are proposed to detect the pattern of breast cancer. In Abdi and Giveki [62], erythemato-squamous diseases are diagnosed based on association rules and with the help of support vector machine and particle swarm optimisation. Shih et al. [63]. Applied association rules mining to discover the hidden relationship among patient data, such as gender, age and type of dementia, in a medical centre. The AR mining shortens the time required and provides insight into formulating the care plan for patients with dementia.

6.4 Conclusions In summary, this chapter reviews the overview, significance and applications of data mining and AI techniques. It is found that data mining techniques are becoming more critical in transforming a vast amount of information into practical knowledge. The use of AI techniques such as fuzzy logic, CBR and GA can help facilitate decisionmaking in the healthcare industry to improve the effectiveness and efficiency in healthcare planning, increasing the accuracy in disease diagnosis and better allocating healthcare resources. By doing so, fast-responsive and proactive healthcare services can be delivered to the elderly to satisfy their individual needs. The future study can focus on the research directions in using a hybrid approach to overcome the shortage of specific types of AI and data mining techniques to enhance further the accuracy and ability in health management, health prediction, and disease diagnosis.

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Chapter 7

Domesticating Homecare Services

7.1 Introduction Ageing population becomes a social and economic issue concerned by the whole world. The advancement in medical treatment and technology causes a longer lifespan for people [1]. The increasing ageing population proportion brings pressure to the governments on medical spending and elderly policy planning. Hong Kong, which is well-known as one of the regions of high life expectancy worldwide, also faces a significant challenge due to the ageing population. The phenomenon of the ageing population causes tremendous impacts on both the society and economy. The increasing number of elderly with chronic illness in Hong Kong raises the need for long-term healthcare services in society and hence brings significant pressure on healthcare service providers. In parallel with an ageing population, it is found that there is an increasing number of elderly with non-communicable diseases [2]. The elderly become weaker, and they may not be able to take care well of themselves. They may also have different problems such as low walking speed, muscle weakness, and balance deficits [3]. With these weaknesses, providing healthcare support at the home of the elderly is essential for maintaining their safety in daily life. There are two main types of elderly healthcare services in Hong Kong to take care of their daily life: nursing homes and domestic homecare nursing service delivery. Nursing homes provide residential and personal care, meals, and primary medical care to the elderly with poor health or disabilities [4, 5]. Nursing staff in the nursing homes provides 24-h close monitoring service to the elderly [6]. On the other hand, home care nursing service delivery provides different community support services to the elderly [7]. The services are provided according to their individual needs to work out the concepts “ageing in place” and “continuum of care” [8, 9]. Initiated by the government, the elderly can continue living in the community with support to fulfil their daily needs. In general, the elderly with slight health problems prefer to receive homecare nursing services delivery rather than staying at nursing homes [10, 11]. The demand for home care nursing services has been increased because of a need © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_7

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for professional medical services instead of inadequate care from family members. Most families of the elderly need to work outside, and thus they may not be able to take care of the elderly at home [12]. Choosing home care nursing services delivery rather than nursing homes is also due to embedded familyism [13]. The traditional culture highly influences some families that the younger family members should take care of the elder members and should not leave the elderly in nursing homes. In addition, they prefer to have skilful carers to carry out any home-health procedures [14]. Therefore, domestic homecare nursing service increases in demand as nurses can visit their homes for a health check and providing support without leaving the elderly in the nursing homes. In past research, Siu et al. [15] proposed using artificial intelligence to generate routing problems to give a suitable solution. This can reduce the cost of the operation, increase the satisfaction of the staff, and give a better service quality. Therefore, a systematic approach for scheduling the nursing staff is necessary to improve the current situation. In this chapter, a domestic homecare service planning system (DHSPS) is designed by integrating a Genetic Algorithm (GA) in domestic care service planning. It is expected that more effective nursing staff schedules can be formulated to reduce the operation cost for the service providers. The rest of the chapter is organised as follows: Sect. 7.2 presents the research methodology of the DHSPS. Section 7.3 presents a case study on how the DHSPS can be applied to a domestic homecare service centre. Section 7.4 presents the result and discussion, while Sect. 7.5 gives the conclusion of the chapter.

7.2 Research Methodology of Domesticating Homecare Services In this section, the domestic homecare service planning system (DHSPS) aims to arrange for nursing staff to deliver home care nursing service. The DHSPS consists of two modules: the front-end homecare service request module and the back-end homecare service planning module. Figure 7.1 shows the architecture of the DHSPS.

7.2.1 Front-End Homecare Service Request Module In the front-end module, service requests and elderly information are collected through a mobile application. The elderly who want to have any homecare services should first register in the mobile application with their particulars, such as name, age, address and medical records. Then, the elderly can make reservations via the mobile application when they prefer to have any domestic homecare services. On the other hand, essential information from the domestic home care service providers is also collected, including the staffing level and type of services to be provided.

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Fig. 7.1 The system architecture of the DHSPS

It is important to obtain travelling time and distance among different locations to design the work plan of nursing staff in providing domestic care services. Therefore, to present the travelling time between each point clearly, a matrix form is used so that the system can select the required time effectively by the corresponding rows and columns. Figure 7.2 shows an example of a travelling time matrix. Besides handling the travelling time, service time in each location should also be recorded. As the services needed of each elderly are different, the data of time required of each service and services delivered to each elderly should be inputted in the system so that the total service time can be determined.

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Fig. 7.2 The example of the service time matrix

7.2.2 Back-End Homecare Service Planning Module The homecare service planning activities are delivered in this module by extracting relevant data collected in the front-end module. The data include the nursing staff, locations of the elderly, the corresponding service time, and travelling time between locations. This module consists of two parts: (i) Model formulation and (ii) GA-based service delivery planning. (i)

Model Formulation

A mathematical model is first formulated in this section to determine the work plan for nursing staff scheduling. Table 7.1 shows the notation of the model, while the model is formulated by Eqs. (7.1)—(8) below. Five assumptions are made to formulate the model and simplify the problem. First, the delivery load is neglected. It is assumed that the company mainly focuses on elderly care training and some forms of basic personal care, which do not involve the consumption of materials. Second, all nurses have the same travelling speeds, and therefore the speeds are assumed to be constant. Third, it is assumed that the time estimation tool used in the system is accurate. Table 7.1 Notation table Notation

Definition

L

Set of all nodes, including 1 deport in the service centre and n locations of the elderly

R

Set of all nursing staff

tj s t ij

t

Service time at the location of the elderly j, where j ∈ L Estimated travelling time from location i to j, where i, j ∈ L

Ti

The upper time limit for providing home care services for the elderly i, where i ∈ L

Dmax

Maximum total working time in each day

Dmin

Minimum total working time in each day

αr

The time adjustment factor for staff r to perform the services, where r ∈ R

αj

Intermediate variable for prohibiting sub-tours; can be interpreted as the position of node j in the route, where j ∈ L

X ij r

Binary variable when staff r travel from location i to j and serves the elderly j, where r∈R

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71

Fourth, all requests from the elderly must be satisfied. Lastly, the time spent on waiting for public transport or waiting for a lift is neglected.    t sj × αr + titj xirj

min

(7.1)

(i, j)∈L r ∈R

s.t.  i∈L

r xik −



= 0 ∀k ∈ L , r ∈ R, i = j

(7.2)

j∈L

 

xirj = 1 i = j

(7.3)

(i, j)∈L r ∈R

Dmin ≤

tis ≤ Ti ∀i ∈ L

(7.4)

   t sj + titj xirj ≤ Dmax

(7.5)

(i, j)∈L



α j ≥ αi + 1 − n 1 −



 xirj

∀i, j ∈ L

(7.6)

r ∈R

αj ≥ 0 ∀ j ∈ L

(7.7)

xirj ∈ {0, 1}

(7.8)

In (7.1), the objective function is to minimise the total time T, where T is the total travelling time and the total service time. The equation (t j s × α r + t ij t ) calculates the service time of nursing staff r in a particular service location and the time taken to travel from this particular location to the next service location. There are different service times for different levels of nursing staff. Senior nursing staff may perform tasks in a shorter time, while part-time or junior nursing staff may take longer to perform the same tasks. To determine which staff are responsible for the route, a time adjustment factor for staff r to perform services is included in the objective function. Its summation represents all possible combinations between each point in the whole system with their particular service time. The binary variable is the decision variable, which determines whether that combination is used in the solution. When the binary variable is 1, this indicates that the route taken and (tjs × αr + tijt) is valid in the solution. On the other hand, if the binary variable is 0, the route is not selected in the solution. The model is subject to some constraints. Constraint (7.2) controls that the same nurse is responsible for providing services to all locations assigned in the route. This equation can ensure whether the same nurse serves all locations in the route. For

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instance, it can avoid nurse A arriving at the location while nurse B leaves the same location. Constraint (7.3) limits that all elderly are served once only. If the binary variable is 0, the nurser does not serve the elderly in a particular route. The binary variable will be one of the nurses is taking that route and will serve the elderly. The sum of binary variables of all nurses in a particular location should be equal to 1 to ensure that only one nurse visits a particular location. Constraint (7.4) provides the allowable total service time for each elderly. Regarding the service time provided by the service centres, there is a range of service time, and limits are set according to the range with a buffer period. Constraint (7.5) limits the minimum and maximum working time of each nursing staff to provide services within a reasonable time. The working time should be within the standard range given by the service centres and can vary from different service providers. Constraint (7.6) prohibits a subtour in the model by creating an intermediate variable αj . Subtours are tours on a subset of less than the number of nodes, which mean that the tours are not in a single direction. Constraint (7.7) and Constraint (7.8) define αj and x ij r as a non-negative integer and a binary variable, respectively. (ii)

GA-Based Service Delivery Planning

After the mathematical model is formulated, the GA is adopted to generate the domestic care service work plan by considering the optimal total time, i.e. the sum of travelling time and service time. Some significant steps are involved in the GA, including (i) chromosome encoding, (ii) population initialisation and fitness function evaluation, (iii) chromosome crossover, mutation and replacement, and (iv) termination criteria. Chromosome encoding The most important step in applying the GA is chromosome encoding. A chromosome consisting of several genes presents a possible solution to a problem. The chromosome helps create different offspring for the next generation of solution. Figure 7.3 presents the encoded chromosome of the model, in which the chromosome is divided into three segments: travel division, travel sequence and staff allocation. A binary number is used in the travel division segment to indicate the different point of locations to form a route. A nursing staff continue the tour to another service location if “0” appears in the corresponding gene. Otherwise, the tour ends after serving the location showing “1” in the gene. The length of genes in this segment equals the total number of locations of the elderly in the system. In the travel sequence

Fig. 7.3 Chromosome encoding for service delivery planning

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73

segment, real integer numbers are applied to indicate the service location ID. It shows the sequence for the nursing staff to deliver services. Like the travel division segment, the length of genes in this segment equals the total number of locations of the elderly in the system. Lastly, in the staff allocation segment, real integer numbers are applied to represent which tour is assigned to staff r. The length of the segment is equal to the total number of staff on duty only. If the tour number assigned to the staff is larger than the total number of tours, no duty is required on the staff. This situation is applied to part-time staff only. As shown in Fig. 7.3, the first travel route covers four locations with the sequence of 13, 50, 24 and 32, and this route is allocated to staff no. 3. Population initialisation and fitness function evaluation Initial chromosomes are created for the generation of the GA. This set is a feasible solution that can help create new offspring for the next generation. The length of the chromosome depends on the number of segments and genes in the problem. In this case, the length of a chromosome equals 2n + r. The initial chromosomes are created until the population size requirement is met. The population size is the control of the number of chromosomes selected. Typically, the parent chromosomes are selected stochastically from the case library and converted into chromosomes. Afterwards, the fitness function of the chromosomes is evaluated. In this stage, the suitability of the chromosomes is evaluated based on Eq. (7.1). The GA will find the optimal solution from the initial chromosomes. With the increased dimension considered, the fitness function is anticipated to be high. The model reduces the opportunity of several combinations of attributes to be chosen because of the high fitness function. Chromosome crossover, mutation and replacement Crossover is the genetic operator which creates diversity among the chromosome offspring. The purpose of crossover is to create new offspring by exchanging the chosen genes between chromosomes. The most commonly used crossover types are: (i) single-point crossover, which exchanges the gene at one point; (ii) multi-point crossover, where genes are exchanged at multiple points; (iii) uniform crossover and half-uniform crossover, which mix the genes from the two parents with a fixed probability; (iv) ordered crossover, which genes are selected randomly from the parent and move to the corresponding locations in the child. Figure 7.4 shows an example of a multi-point crossover. A mutation is also an operator who maintains genetic diversity by forming new offspring. The purpose of mutation is to avoid the solutions falling into local optima by randomly changing the value of binary genes from 0 or 1 or any number for the actual number of genes. The common mutation in the GA changes the gene value from either 0 or 1 under the binary-encoding scheme. The genes are assigned with a random number between 0 and 1. The number that is smaller than a particular value is the chosen gene to mutate. A new random number is also generated, so a new chromosome is formed. For the actual number of genes, a random number is generated to replace the original value. Adjustment is required to prevent duplication of value in the chromosome. Figure 7.5 shows an example of a mutation in the GA.

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Fig. 7.4 An example of multi-point crossover in the GA

Fig. 7.5 An example of multi-point mutation in the GA

Replacement Whenever a better possible offspring is produced, it immediately enters the population, and therefore the population size remains unchanged. In case the new offspring violates any constraints, a penalty is added directly to the fitness value. Otherwise, this operation extracts the chromosomes with the worst objective values in the present pool and replaces the chromosomes with the best objective values in the current mating pool. As a result of crossover and mutation, new offspring are created, and the new chromosomes may generate a better result, so the mating pool offers some better solutions. Replacement can help obtain the best generation of chromosomes from the initial chromosomes and the offspring.

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75

Termination criteria The operation of the GA ends when the termination criteria are met at the predefined setting, for example, the required number of trials and the required run-time. After the first generation is completed, the termination criteria should be checked. Once the termination criteria are met, the chromosome with the best fitness value will be selected as the final solution. The chromosome is then translated into a list of vehicles’ routes in an understandable format to everyone. If the criteria are not met, the operation should continue until they are met.

7.3 Case Study in a Local Domestic Homecare Service Center A case study was conducted in a local domestic homecare service centre. The service centre provides different services, such as children service, active ageing service, and disabled people care. Besides, it also provides mental health services and green living on top of physical services. By providing different elderly services, the elderly can live in their neighbourhood with healthy body conditions. They can take care of themselves to prevent staying in hospitals or nursing homes. There are different teams in the service centre. One of the teams provides home care services for the elderly. The services are delivered according to the preference of the elderly and the situation of the elderly. The service centre offers two main types of services, including primary domestic care and professional medical care. For primary domestic care, the nursing staff, usually with less experience and lower qualifications, must perform elderly personal care and basic housework. The services are usually provided by nursing staff with extensive professional knowledge and experience for professional medical care. The type of services includes medicine delivery, caring treatment, and recovery training. Table 7.2 shows the elderly services provided by the service centre. In total, 20 staff members are being responsible for providing domestic elderly care. Among all, eight of them work as full-time staff and the remaining work in a part-time model. The part-time staff are from the voluntary housewife team in the neighbourhood. They receive professional training before on board to be capable of delivering medical service. For the policy of the part-time staff, they are required to work for three days a week, while the full-time staff work five days a week. Each Table 7.2 Elderly services provided by the service centre

Type of care

Homecare services

Basic domestic care

Personal care Basic homework

Professional medical care

Medicine delivery Caring treatment Recovery training

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staff member works nine hours per day in current practice, with a one-hour lunch break and several short breaks after each service delivery. Currently, the service centre is serving 120 elderly in Hong Kong. The service locations near the centre, the staff, would travel to the locations on foot. If the locations are far away (estimated walking time is longer than 20 min), they will travel by public transports. The staff members must take the documents and serving materials, such as the medical records, medicine, and the material for recovery training, from the centre. After performing all jobs that day, the staff has to go back to the centre to return the documents and material to prevent the risk of losing confidential documents. The senior nurses in the centre prepare the daily roster. In managing the roster, senior nurses must consider the available staff and the service requirement based on their experience and basic geographic information. The service time required by each elderly depends on the service they need. As mentioned above, the service is divided into primary care and medical care. For a basic care service, it generally takes 25 min for each service. For medical care, it usually takes 35 min for each service. However, the time is varied by the same service delivered. Since the service centre wanted to improve its service quality and performance, a pilot run of DHSPS was implemented for planning the nursing staff service schedule. There are two main processes: (i) data extraction and data processing and (ii) developing the GA model for the work plan schedule.

7.3.1 Data Extraction and Data Processing Data was collected from two sources: the domestic homecare service centre and the elderly. Table 7.3 shows the example data extracted from the data sources. For the elderly, data collected was mainly their particulars and types of services requested. On the other hand, data collected in the service centre included nursing staff information, the service locations, and types of service needed. The current daily roster was also collected to evaluate the performance of the current work plan. After collecting all these data, the data was handled and stored in the database. Each service location was assigned a location ID for easy reference to planning for the service delivery schedule. The number was the same as the elderly ID, representing the elderly, the routes and locations. The points could then be detected via Table 7.3 Examples of data from the data sources

Sources of data

Example of data

The elderly

Elderly name and ID, age, gender, address, medical records Service requested, feedback of current services

Service centre

Staff name and ID, education level, experience, expertise Service locations, service time, service types

7.3 Case Study in a Local Domestic Homecare Service Center

77

Google Maps, and the longitude and latitude of the points were recorded. Figure 7.6 shows the geographic locations of all service requests, while Table 7.4 shows some examples of service locations.

Fig. 7.6 Geographic locations of all service requests

Table 7.4 The coordinates of the first 15 service locations Service center

Location ID

Coordinates Latitude

Longitude

0

22.31180

114.2289

Hing on house

1

22.31671

114.2270

Cheung on house

2

22.31699

114.2272

Sun on house

3

22.31723

114.2269

Block 15 Laguna City

4

22.30632

114.2278

Block 34 Laguna City

5

22.30503

114.2285

Block 21 Laguna City

6

22.30505

114.2270

Ping wong house

7

22.30577

114.2362

Ping shing house

8

22.30676

114.2368

Ping shun house

9

22.30683

114.2358

Tao Nga house

10

22.30603

114.2383

Hong Nga house

11

22.30515

114.2403

Kwong Nga house

12

22.30313

114.2408

Kwong Yat house

13

22.32856

114.2408

Hei Wah house

14

22.32006

114.2223

Wun Wah house

15

22.32024

114.2212

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7 Domesticating Homecare Services

Fig. 7.7 Excel VBA code for travelling time calculation

With the location information of all the stops, a distance matrix was calculated. Instead of manually checking one by one, Excel VBA and route planning function in Google Maps were used to estimate the travelling time. Figure 7.7 shows the Excel VBA code to calculate the travelling time between each pair of locations. If the walking distance is less than 20 min, walking will be chosen as the travelling method. If the walking distance is longer than 20 min, the fastest way of transportation will be chosen instead. After collecting all the travelling times, a matrix was used to represent the times. Figure 7.8 shows the matrix for the point-to-point travelling time between different locations. For example, the travelling time from locations 1 to 2 was found to be 680 s. In addition to travelling time, a certain amount of time is required when the nursing staff perform duties at the elderly home. The service time varies according to the types of service required by the elderly. Currently, the service centre offers six types of services to the elderly, including meal delivery, basic housework, personal care, recovery training, and treatment for the weak and disabled. Their corresponding standard completion time is 5 min, 30 min, 15 min, 40 min, 30 min, and 35 min. Based on the needs of the elderly, the nursing staff may need to perform more than one type of service during the visit. Table 7.5 shows the sample service requests by the elderly.

Fig. 7.8 Matrix of travelling time

7.3 Case Study in a Local Domestic Homecare Service Center

79

Table 7.5 Sample service requests by the elderly

1 2 3 4 5

Meal delivery (5 min)

Basic housework (30 min)

√

√

√

√

√

√

√

√

√

√

√

Treatment (Weak) (30 min) √

Treatment (Disabled) (35 min)

65 50 √

√ √

√

√

50 85 35 45

√

8

Total Time (mins) 30

√

√

7

10

Recovery training (40 min)

√

6

9

Personal care (15 min)

40 35 45

7.3.2 Development of the Genetic Algorithm Model for Work Plan Scheduling Based on the model formulation in Sect. 7.2, the GA was then used to generate the homecare service schedule. It calculated all possible combinations between each point in the whole system with their particular service time. There are three parts to encode the chromosome: travel division segment, travel sequence segment and staff allocation segment. The gene can be either 0 or 1. One represents the termination point of that tour and zero otherwise in the travel division segment. The value is set randomly for the travel sequence segment and staff allocation segment before simulating the model. During the simulation, the sequence is changed to obtain a better result from the original chromosome. One of the significant constraints is that one staff member has limited working time per day. In total, the staff can work for nine hours per day with a 60-min lunch break, two 25-min breaks, and travelling time. Therefore, the total time for providing services and travelling in a day cannot exceed 430 min. If the total travelling time exceeds 430 min, the previous point will be the endpoint of that route, and a new staff member will be assigned to the new tour. Figure 7.8 presents an example of chromosome encoding, with three nursing staff and six older adults have requested homecare services. The first six genes in the chromosome represent the travel division of the route, corresponding to the first three genes in the travel sequence segment. Since each tour starts and ends at the service centre, the first route shown in Fig. 7.9 is denoted as the centre → 13 → 20 → 24 → Centre; this is assigned to Staff 3. Staff 1 takes the second tour, which is centre → 32 → 7 → 10 → Centre. As only two tours are sufficient to satisfy the demand, no duty is assigned to Staff 2 on this day. The GA-based Evolver simulation software was used to generate the time of each route to determine the number of nursing staff required for serving the elderly. The

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7 Domesticating Homecare Services

Fig. 7.9 An example of chromosome encoding

Table 7.6 The GA parameter settings

Parameters

Settings

Population size

50 / 100

Number of generations

8,000 / 15,000 / 20,000

Crossover rate

0.7 / 0.9

Mutation rate

0.05 / 0.1

result generated could provide decision support in designing appropriate schedules for nursing staff. It is commonly known that the GA parameter settings on population size, number of generations, crossover rate and mutation rate would affect the result generated. Hence, different combinations of the GA parameter settings were tested to obtain the best setting. Table 7.6 shows the GA parameter settings. In this study, the selected population sizes were 50 and 100 for analysis. After setting the population size, the sequence chromosome started to change to search for the more optimal solution using the GA. New cases were formed once a better result was found. When reaching the termination criteria, the GA operation was then stopped. By using the Roulette-Wheel selection, a larger fitness value resulted in a higher probability to be selected. After the chromosomes were selected, crossover and mutation would occur to generate a better solution. Crossover rates of 0.7 and 0.9 and mutation rates of 0.05 and 0.1 were chosen for testing. In addition, to further find a better solution during trials, the number of generations in each simulation could also be set. A different number of generations might bring different answers. It was expected that a larger number of generations would bring the smallest fitness value. However, a longer time might be required. 8,000, 15,000, and 20,000 were used in the number of trials for testing the results. The settings that could generate the best result were selected after analysing the simulation results.

7.4 Results and Discussion

81

7.4 Results and Discussion Twenty-four combinations in the GA model were simulated to determine the best routing schedule of the nursing staff in the domestic homecare service centre, and each combination was generated by ten times. It was hoped to find out the best parameter combination to generate the GA result so that the best achievement could be obtained during the daily operation of the service centre. Table 7.7 shows the combination of parameters tested during the GA generation. Analysis with different parameters was done in the following part of this report. The analysis was based on five factors: the best fitness, the average values, the largest values, the standard deviations, and the average running time among the ten generations. Table 7.8 and Table 7.9 summarise the simulation results according to the different population sizes. Table 7.7 The combinations of settings in the GA generation Setting

Population

Crossover rate

Mutation rate

Number of trials

1

50

0.7

0.05

8,000

2

50

0.7

0.05

15,000

3

50

0.7

0.05

20,000

4

50

0.9

0.05

8,000

5

50

0.9

0.05

15,000

6

50

0.9

0.05

20,000

7

50

0.7

0.1

8,000

8

50

0.7

0.1

15,000

9

50

0.7

0.1

20,000

10

50

0.9

0.1

8,000

11

50

0.9

0.1

15,000

12

50

0.9

0.1

20,000

13

100

0.7

0.05

8,000

14

100

0.7

0.05

15,000

15

100

0.7

0.05

20,000

16

100

0.9

0.05

8,000

17

100

0.9

0.05

15,000

18

100

0.9

0.05

20,000

19

100

0.7

0.1

8,000

20

100

0.7

0.1

15,000

21

100

0.7

0.1

20,000

22

100

0.9

0.1

8,000

23

100

0.9

0.1

15,000

24

100

0.9

0.1

20,000

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7 Domesticating Homecare Services

Table 7.8 GA result of population size = 50 Best fitness

Average value

Largest value

Standard deviation

Average running time (s)

Number of generations = 8,000 C.R. = 0.7, M.R. = 0.05

5,574

5,644

5,736

51.32

126

C.R. = 0.7, M.R. = 0.1

5,712

5,748

5,794

28.48

79

C.R. = 0.9, M.R. = 0.05

5,458

5,532

5,596

41.25

113

C.R. = 0.9, M.R. = 0.1

5,650

5,687

5,701

14.76

96

Number of generations = 15,000 C.R. = 0.7, M.R. = 0.05

5,502

5,582

5,685

50.01

198

C.R. = 0.7, M.R. = 0.1

5,640

5,716

5,777

45.01

175

C.R. = 0.9, M.R. = 0.05

5,353

5,438

5,494

48.49

210

C.R. = 0.9, M.R. = 0.1

5,559

5,604

5,664

28.65

175

Number of generations = 20,000 C.R. = 0.7, M.R. = 0.05

5,464

5,532

5,637

49.89

258

C.R. = 0.7, M.R. = 0.1

5,578

5,674

5,709

35.34

217

C.R. = 0.9, M.R. = 0.05

5,327

5,406

5,447

21.14

286

C.R. = 0.9, M.R. = 0.1

5,556

5,613

5,651

23.38

213

Table 7.10 summarises the average results of the total travelling time with different parameter settings. As shown in the table, it is found that the best average result is between 5,400–5,500, in which the crossover rate of 0.9 and mutation rate of 0.05 had a relatively better performance. In summary, a population size of 50 with a 0.9 crossover rate, 0.05 mutation rate and 15,000 generations gave the shortest travelling time. Hence, this setting was selected to generate the service delivery plan. In the illustrated example, 18 nursing staff were needed to fulfil the service requirements of all 120 locations. Compared with manual planning based on the experience of the route planner, the results obtained in the proposed system might lead to decreased travelling costs. Figure 7.10 shows two sample results of the service delivery plan generated, while Fig. 7.11 shows the travel sequence and total time for route 1. The total time of this route is 387 min, which the module can automatically calculate. As there are 20 staff members, comprising eight full-time staff and 12

7.4 Results and Discussion

83

Table 7.9 GA result of population size = 100 Best fitness

Average value

Largest value

Standard deviation

Average running time (s)

Number of generations = 8,000 C.R. = 0.7, M.R. = 0.05

5,679

5,713

5,750

24.73

102

C.R. = 0.7, M.R. = 0.1

5,743

5,789

5,844

28.27

101

C.R. = 0.9, M.R. = 0.05

5,558

5,620

5,661

30.84

121

C.R. = 0.9, M.R. = 0.1

5,670

5,711

5,777

29.39

95

Number of generations = 15,000 C.R. = 0.7, M.R. = 0.05

5,500

5,604

5,673

50.33

203

C.R. = 0.7, M.R. = 0.1

5,701

5,746

5,803

27.82

171

C.R. = 0.9, M.R. = 0.05

5,459

5,534

5,576

30.82

218

C.R. = 0.9, M.R. = 0.1

5,579

5,647

5,681

29.16

167

Number of generations = 20,000 C.R. = 0.7, M.R. = 0.05

5,560

5,610

5,705

41.3

250

C.R. = 0.7, M.R. = 0.1

5,684

5,735

5,792

30.16

217

C.R. = 0.9, M.R. = 0.05

5,436

5,486

5,561

39.73

274

C.R. = 0.9, M.R. = 0.1

5,612

5,649

5,677

22.25

227

Table 7.10 Summary of average values of different parameters Population/No. of trials

Crossover/mutation rate 0.7/0.05

0.7/0.1

0.9/0.05

0.9/0.1

50/8,000

5,644

5,748

5,532

5,687

50/15,000

5,582

5,716

5,438

5,604

50/20,000

5,532

5,674

5,406

5,613

100/80,000

5,713

5,789

5,620

5,711

100/15,000

5,604

5,746

5,534

5,647

100/20,000

5,610

5,735

5,486

5,649

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7 Domesticating Homecare Services

(a)

(b)

Fig. 7.10 Sample result of service delivery plan generated for a Route 1, and b Route 2

Fig. 7.11 Travel sequence and the total time of Route 1

part-time staff members, two can be absent on the day with the above roster. The cost of hiring staff can be thus reduced. In addition, the regular leave system for staff can become more flexible since the number of staff needed to deliver elderly services decreases.

7.5 Conclusions Due to the increasing ageing population in Hong Kong, the need for domestic care nursing services is increasing. For example, the elderly with chronic diseases need more care. While with the elderly preferences of staying home, domestic care nursing service delivery is increased in demand. However, in domestic care, nursing services, insufficient qualified and skilful nursing staff for serving the elderly is always the key concern of the domestic homecare service providers. Meanwhile, without a systematic way to schedule the work plan of nursing staff, the nursing staff may suffer high pressure to complete all duties in a tight schedule. It may result in poor working

7.5 Conclusions

85

performance and also affect the service satisfaction of the elderly. It is important to have a systematic approach for facilitating the decision making of staff in scheduling the roster of domestic care nursing staff to tackle these problems. In this study, a Domestic Homecare Service Planning System (DHSPS) is proposed. By applying the GA in the proposed system to find out the shortest total time, the work plan with schedules of the domestic care nursing staff is generated. It also reduces overtime working of the domestic care nursing staff. A pilot study is conducted in a domestic healthcare service centre to formulate the work plan and validate the feasibility of the DHSPS. The results indicated that the work satisfaction of the nursing staff is increased due to better planning on travelling routes. In this study, the scheduling plan is generated by assuming that the traffic conditions are good. Further work will be focused on designing a dynamic solution that can adjust the service schedule by considering real-time traffic conditions, such as traffic jams in rush hour, and updates to the work situation of nursing staff.

References 1. Suzuki, T. (2018). Health status of older adults living in the community in Japan: Recent changes and significance in the super-aged society. Geriatrics & Gerontology International, 18(5), 667–677. 2. Goeppel, C., Frenz, P., Tinnemann, P., & Grabenhenrich, L. (2014). Universal health coverage for elderly people with non-communicable diseases in low-income and middle-income countries: A cross-sectional analysis. The Lancet, 384, S6. 3. Lauretani, F., Maggio, M., Ticinesi, A., Tana, C., Prati, B., Gionti, L., Nouvenne, A., & Meschi, T. (2018). Muscle weakness, cognitive impairment and their interaction on altered balance in elderly outpatients: Results from the TRIP observational study. Clinical Interventions in Aging, 13, 1437. 4. Gilissen, J., Pivodic, L., Smets, T., Gastmans, C., Vander Stichele, R., Deliens, L., & Van den Block, L. (2017). Preconditions for successful advance care planning in nursing homes: A systematic review. International Journal of Nursing Studies, 66, 47–59. 5. Sanerma, P., Miettinen, S., Paavilainen, E., & Åstedt-Kurki, P. (2020). A client-centered approach in home care for older persons–an integrative review. Scandinavian Journal of Primary Health Care, 38(4), 369–380. 6. Valizadeh, L., Zamanzadeh, V., Saber, S., & Kianian, T. (2018). Challenges and barriers faced by home care centers: An integrative review. Medical-Surgical Nursing Journal, 7(3), e83486. 7. Tang, V., Choy, K. L., Ho, G. T., Lam, H. Y., & Tsang, Y. P. (2019). An IoMT-based geriatric care management system for achieving smart health in nursing homes. Industrial Management & Data Systems, 119(8), 1819–1840. 8. Weil, J., & Smith, E. (2016). Revaluating aging in place: From traditional definitions to the continuum of care. Working With Older People, 20(4), 223–230. 9. Pani-Harreman, K. E., van Duren, J. M., Kempen, G. I., & Bours, G. J. (2021). The conceptualisation of vital communities related to ageing in place: a scoping review. European Journal of Ageing, 1–14. 10. Gudnadottir, M., Bjornsdottir, K., & Jonsdottir, S. (2019). Perception of integrated practice in home care services. Journal of Integrated Care, 27(1), 73–82. 11. Aase, I., Ree, E., Johannessen, T., Strømme, T., Ullebust, B., Holen-Rabbersvik, E., Thomsen, L. H., Schibevaag, L., van de Bovenkamp, H., & Wiig, S. (2021). Talking about quality: How ‘quality’is conceptualised in nursing homes and homecare. BMC Health Services Research, 21(1), 1–12.

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12. Valizadeh, L., Zamanzadeh, V., Saber, S., & Kianian, T. (2018). Challenges and barriers faced by home care centers: An integrative review. Medical-Surgical Nursing Journal, 7(3), 1–9. 13. Hernández, M. M., & Bámaca-Colbert, M. Y. (2016). A behavioral process model of familism. Journal of Family Theory & Review, 8(4), 463–483. 14. Hittle, B., Agbonifo, N., Suarez, R., Davis, K. G., & Ballard, T. (2016). Complexity of occupational exposures for home healthcare workers: nurses vs. home health aides. Journal of nursing management, 24(8), 1071–1079. 15. Siu, P. K., Choy, K. L., & Lam, H. Y. (2018). an intelligent service planning system for effective home care service scheduling. In 2018 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1–6).

Chapter 8

Case Study in Fall Prevention in Indoor Environments

8.1 Introduction Due to the increasing ageing population issue, more and more countries such as UK, Japan, New Zealand, and Singapore have considered long-term care solutions, i.e. “Ageing in Place”, in their policy. “Ageing in Place” refers to remain the elderly living in the community independently and comfortably rather than moving than in residential care [1, 2]. Past research claims that the concept of “Ageing in Place” enables the independence, autonomy and connection to a social community of the elderly and avoids the costly option of institutional care [1, 3, 4]. Hong Kong also faces the same problem of ageing problem. Since 1997, the government has focused on elderly care as one of the critical components in policy address with the objective of “Promoting elders’ sense of belonging, sense of security and sense of worthiness”. In 2017, the government emphasised that healthcare organisations should support “ageing in place as the core, institutional care as a backup”. Therefore, “Ageing in Place” is the focus for the government and raised public awareness. However, in Hong Kong, the elderly’s family members are usually busy working and do not have sufficient time to take care of the elderly [5]. As a consequence, the elderly are mostly living alone in their home. The elderly living alone are more likely to have accidents, feel depressed, and finally, enter the elderly home due to poor health status problems compared with the elderly living with others [6, 7]. According to the Census and Statistics Department [8], diseases such as hypertension, high cholesterol, diabetes mellitus, heart diseases and stroke are the common diseases the elderly may suffer from at their home. Among the various accidents, fall is a common accident and the leading cause of fatal and non-fatal injuries among the elderly when they stay at home alone or go out independently [9, 10]. Even worse, after the falling, the elderly may worry that fall again soon and reduce their willingness to participate in the activities or exercise. Due to a lack of training of muscles for a long-time, the functional ability of the elderly and the flexibility and strength will lose and finally affect their quality of life [11]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_8

87

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8 Case Study in Fall Prevention in Indoor Environments

To prevent falls and maintain autonomy, fall prevention exercise and strengthening physical conditions are being promoted, focusing on balance and strength training to reduce the risk of falling. Although the accidents rate was reduced after launching such a program, the government provided only less support to the elderly who live in the non-rental estates. With the rapid development of advanced technologies, it is believed that the technologies such as the Internet of Things (IoT), wireless technology and cloud computing can create a better life for the elderly regarding health monitoring and health assessment. Therefore, in this chapter, an IoT-based intelligent health system with smart devices (IIHS) is designed by integrating IoT, fog computing and artificial intelligence for achieving better health monitoring, planning and forecasting. Through the IIHS, it allows the elderly to have fall prevention and strengthening exercise safety and enhance their balancing ability to work out to achieve the continuous monitoring and improvement of their health condition. The rest of the chapter is organised as follows: Sect. 8.2 presents the research methodology of the IIHS. Section 8.3 presents a case study on how the IIHS can be applied to a domestic homecare service centre. Section 8.4 presents the discussion, while Sect. 8.5 gives the conclusion of the chapter.

8.2 Research Methodology In this section, the IoT-based intelligent health system (IIHS) is designed which aims to (i) enhance the balance and strength of muscles through the development of the balance board and (ii) monitor the health status of the elderly in the system platform. In the IIHS, three modules are involved: the data collection and consultation module, balance board development module, and health platform development module.

8.2.1 Data Collection and Consultation Module It is important to understand the reasons that may lead to the fall in daily living to construct the proposed IIHS. Professional knowledge, such as different diseases information and the impacts on the elderly, and the elderly’s situations under distinct factors are collected. All of this information is collected through consultations with physiotherapists. Based on the collected data, it is identified that there are two types of workout exercises for improving balance, including (i) balance workout for a sitter and (ii) balance workout for a walker. The balanced workout for a sitter aims to improve shaky balance by the elderly who can sit up in an armed chair independently. Six postures are involved in this workforce, including (i) ready to stand posture, (ii) ready to stand posture withholding, (iii) leg marching, (iv) leg marching withholding, (v) seated straight-leg raise and (vi) seated straight-leg raise withholding. Table 8.1 shows the details of each posture. For the

8.2 Research Methodology

89

Table 8.1 Posture for the balance workout for a sitter No Posture 1. 2.

Training Focus

Posture details

Ready to stand posture Weight shifting abilReady to ity, Shoulder stability, stand posture Elbow, Hip and knee withholding extensors strength

3.

Leg marching

4.

Leg march- Hip flexors (a group ing withhold- of muscles at the hip ing and thigh)

5.

Seated straight-leg raise

6.

Seated straight-leg raise

Quadriceps (knee extensors) strengthening

Quadriceps strengthening, Hips flexors

balanced workout for a walker, there are three different levels of difficulty: beginner, immediate, and advanced. The elderly need to meet different requirements according to their difficulty. For the beginner, only a sturdy chair is used for performing balance training. For the immediate, it is suitable for e indoor walker in which a sturdy chair and wall are needed for helping improve static balance. It is suitable for outdoor walkers in the advanced level, and the chair and wall are optional. Sixteen postures are involved in this set of workout exercises as shown in Table 8.2. All this information is collected and stored in the cloud database for hardware and software development in the following two modules.

8.2.2 Balance Board Development Module In this module, hardware, i.e. balance board, is designed and developed with traditional load cells and additional six infra-red detectors and two infra-red sensors. Figure 8.1 shows the product design of the balance board. Instead of the one set of a load cell, four sets of load cell are installed at the four corners of the hardware to evenly measure the bodyweight of the elderly using the centre of gravity approach. The centre of gravity in this scenario is defined as (x, y), and the force applied in each load cell are FA , FB , FC and FD . Fs is the summation of the FA , FB , FC and FD . With the distance among the load cells, i.e. w × l, the coordinate of the centre of gravity can be calculated as the following equations: XCG

1 (−FA + FB + FC − FD ) = 2 Fs

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8 Case Study in Fall Prevention in Indoor Environments

Table 8.2 Posture for the balanced workout for a walker No. Posture

Training Focus

1. Weight Shifting

Static standing Balance, Weight shifting, Hip abductors

Posture details

2. Mini Squats 3. Mini Squats Knee extensors, with Hold- Hip extensors ing

4. Sit to Stand Hip extensors, Calf (ankle plantarflexor), Quadriceps (knee extensors) strengths

5. Toe Stand

6. Toe Stand Knee extensor, Hip with Holdextensor, Calf (aning kle plantarflexor)

7. Step Up I

Knee extensor, Hip extensor

8. Step Up II Knee extensor, Hip extensor

9. Hip Extension Hip extensors (Glu10. Hip Extentei, hamstrings) sion with Holding

(continued)

8.2 Research Methodology

91

Table 8.2 (continued) 11. Hamstrings Curl 12. Hamstrings Hamstrings muscle Curl with Holding

13. Single Leg Quadriceps, Hip exStand tensors, Calf

14 Ankle Rocking

Dorsiflexor muscle, Calf (Plantar Flexor)

15. Sideway Leg Raises 16. Sideway Leg Raise Hip abductors with Holding

Fig. 8.1 Design of balance board

YC G =

(w/2)(FA + FB − FC − FD ) Fs

With the calculated centre of gravity, such data can be stored in the cloud database every 30 ms. The users can use their smartphone to read the information regarding their weight, speed score, balance score and physical exercise patterns. Data processing processes including sampling, filtering, and analysing are performed to reduce the computational load in the cloud database.

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Apart from the load cell, six IoT sensors, i.e. four infrared object detectors and two infrared distance detectors, are also installed in the balance board, as shown in Figs. 8.2 and 8.3, to capture the feet motion data users. When the elderly are doing the workout exercise, they must step their feet on the balance board. At that moment, the six sensors detect and count their number of stepping accurately. Through the smartphone, the uses can also read such information in the App. As the target users of the balance board are the elderly, safety requirements should be considered in its design, and the anti-slippery design on the top surface is needed for increasing the friction between the feet of users and the balance board to prevent falling during the exercise.

Fig. 8.2 Position of six infra-red detectors in the balance board

Fig. 8.3 Distance detection in feet movement

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8.2.3 Health Platform Development Module Cloud computing with fog computing and edge computing models is applied to centralise and analyse the data collected by the IoT devices. Through the integration of such technologies, the architecture of the IoT can be established for providing the functionalities of health monitoring, balance assessment, status sharing and training exercise, as shown in Fig. 8.4. Firstly, the edge devices such as balance load and smart wearable devices collect various health data. Such data are then transferred to the intermediate layer, i.e. fog layer, to provide storage, computing and connectivity services. After that, the application programming interface (API) is also ready for interested partners to implement the proposed platform for health and balance improvement.

8.3 Case Study In this section, the proposed IoT-based intelligent health system (IIHS) was applied to a home care organisation to monitor and record exercise training of the users in their homes. All training results collected from the balance boards will be collected, transferred, and stored in the cloud database for further bug data analytics. To implement the proposed IIHS platform, each user must create an account that contains their personal information such as the name, gender, exercise suitability and health record. Figure 8.5 show the user interface for registration. After successful registration, users with balance boards can perform the primary functions in the IIHS platform, i.e. test, exercise and scorers. The details of each function are discussed in the following.

Fig. 8.4 Architecture for cloud computing between balance boards and cloud

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Fig. 8.5 User interface for registration

8.3.1 Test The calibration will be taken to understand the user’s health status before starting the exercise training test. The user is required to stand on the balance boards to perform a set of exercises to complete the calibration. At the same time, the balance board will collect the number of counts, the centre of gravity points and heart rate of the user. These data are also displayed in the IIHS platform to let the user understand their performance. After completing one set of exercise, the pop-up questions will appear to ask for the users’ health status, such as “Do you feel dizzy now?” and “Are you hard to breathe now?”. A test report will be generated to show the level of exercise, test score, weight distribution and static balance of the user. Figure 8.6 shows the user interface to display the test results.

8.3.2 Training and Rehabilitation After the calibration, the user can start the exercise training. It is recommended that the users should do the exercise three times a day, if possible. Once the user feels not well during the exercise, they should stop the exercise and rest. Like the test function, the platform will display the number of counts, the centre of gravity and heart rate during the exercise. In addition, there are four levels of colour to indicate the heartrate: Green colour means the normal heartrate; Yellow colour means the moderate heart rate, but the rate is increasing; Orange colour means the heart rate is slightly high and it is suggested to stop or slow down the exercise, and red colour means the heart

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Fig. 8.6 User interface to display the test results

rate is very high and the users must stop doing any exercise immediately and take a good rest. The users’ performance in each exercise is also recorded and stored on the platform for health review purposes. Apart from the exercise, the user can view a list of demo videos to understand the correct postures for the exercise. Users can also change the exercise level and re-calibrate the exercise again.

8.3.3 Scores The scores page provides complete exercise records and user performances to the home care centre for health review and monitoring purposes. Through the sharing of the collected health data from the IIHS platform, caregivers in the home care centre can use such data to perform health monitoring and assessment tasks. Caregivers need to decide the suitable exercise and the corresponding levels for the users to do. Figure 8.7 shows the score for a particular day and the cumulative score of the user. Figure 8.8 shows the summary of the exercises and levels which the users have completed. In addition, the caregivers can identify any abnormalities that occurred in the health status of the user. Ultimately, with the proposed IIHS platform and smart balance boards, it can help the elderly to strengthen the balancing ability of the elderly to address and prevent falling occurred at their home.

8.4 Discussion This study aims to enhance the health conditions and better balancing ability of the elderly for addressing fall prevention and achieving smart elderly care. The proposed IIHS platform allows the elderly to have exercise training at their home through the

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Fig. 8.7 User interface for the score page

Fig. 8.8 User interface for the exercise levels summary

development of balance boards. In addition, it allows caregivers to keep track of the health status of the elderly by collecting the exercise data. In terms of data accuracy, the developed balance boards with four sets of fullbridge load cells, six infrared object detectors and two infrared distance sensors can capture the legs/feet movements in a real-time manner. Such data can be seamlessly synchronised and displayed on the IIHS platform so that the user can understand

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their exercise performance. With accurate data, caregivers can efficiently conduct comprehensive health assessments to generate fast-responsive actions to the elderly. In addition, it is found that the number of falling cases is reduced after the use of balance boards, especially for the case of the elderly living alone. Most elderly have more confidence and independence after using the balance boards. Moreover, machine learning can be considered to train up the proposed IIHS to improve the performance in detecting the correct posture during the exercise [12]. Starting from collecting the elderly motion data, different features for posture prediction will be extracted, and the profile for each posture will be established. After feature extraction, various machine learning classifiers such as support vector machine, logistic regression, decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis can be applied to classify the posture features and evaluate its performance [13, 14]. The classification results can be used to predict the posture of the elderly. In addition, with the increasing amount of posture data in the proposed IIHS, the detection accuracy in falling can be further increased. Besides aligning with the policies “Ageing in Place”, the proposed IIHS can help promote community care and improve healthcare resources allocation in the home care centre. Instead of assigning the caregivers to face-to-face visit the elderly’s home, the elderly can stand on the balance board to exercise for self-training. Through the status sharing from the IIHS platform, caregivers can monitor the balancing ability and exercise performance of the elderly effectively and efficiently. Under the stress of limited healthcare resources, advanced technologies can help free caregivers to focus on elderly caring tasks. In such a situation, more elderly can enjoy the appropriate healthcare services in the community.

8.5 Conclusion With the promotion of the “Ageing in Place” and community care, an IoT-based intelligent health system with smart devices (IIHS) is designed in this chapter. Firstly, smart IoT hardware, i.e. balance boards, is developed for the elderly to collect health data during the exercise. The elderly need to strengthen their balance ability in daily life, such as walking upstairs, getting up from bed, and carrying groceries in the supermarket, reducing the fall risks. After that, fog computing and artificial intelligence are applied to better health monitoring, planning and forecasting for the elderly. The architecture of fog computing is also provided in this study for collecting, gathering and analysing data from edge devices to cloud centres. Through the case study, it is found that the proposed IIHS platform can reduce the number of falls occurred at the elderly home. In addition, the connection between smart devices and the cloud can enhance communications and interactions with the elderly through the IIHS platform. The proposed IIHS aims to prevent the fall in the indoor environment by performing the exercise using smart IoT hardware. Future research can be focused on adopting various types of sensors such as wearable sensors, ambience sensors, and hybrid model sensors to capture user’s data in an indoor environment. Besides,

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research directions on improving the robustness, accuracy, and sensitivity of fall prevention systems are also important to provide immediate help and reduce the post-fall consequences to patients.

References 1. Wiles, J. L., Leibing, A., Guberman, N., Reeve, J., & Allen, R. E. (2012). The meaning of “aging in place” to older people. The Gerontologist, 52(3), 357–366. 2. Chui, E. (2008). Ageing in place in Hong Kong—Challenges and opportunities in a capitalist Chinese city. Ageing International, 32(3), 167–182. 3. Kan, H. Y., Forsyth, A., & Molinsky, J. (2020). Measuring the built environment for aging in place: A review of neighborhood audit tools. Journal of Planning Literature, 35(2), 180–194. 4. World Health Organization. (2007). Global age-friendly cities: A guide. Retrieved 28 February 2021 from http://www.who.int./ageing/age_friendly_cities_network. 5. CUHK Jockey Club Institute of Ageing. (2016). Report on agewatch index for hong kong 2014. The Hong Kong Jockey Club. 6. Liu, T., Lu, S., Leung, D. K., Sze, L. C., Kwok, W. W., Tang, J. Y., Luo, H., Lum, Y. S. & Wong, G. H. (2020). Adapting the UCLA 3-item loneliness scale for community-based depressive symptoms screening interview among older Chinese: a cross-sectional study. BMJ Open, 10(12), e041921. 7. Chou, K. L., & Chi, I. (2000). Comparison between elderly Chinese living alone and those living with others. Journal of Gerontological Social Work, 33(4), 51–66. 8. Census and Statistics Department. (2017). Thematic Household Survey Report No.58, [Online]. Available: https://www.statistics.gov.hk/pub/B11302582015XXXXB0100.pdf Accessed 18 April, 2021. 9. Oh-Park, M., Doan, T., Dohle, C., Vermiglio-Kohn, V., & Abdou, A. (2021). Technology utilisation in fall prevention. American Journal of Physical Medicine & Rehabilitation, 100(1), 92–99. 10. Wiseman, J. M., Stamper, D. S., Sheridan, E., Caterino, J. M., Quatman-Yates, C. C., & Quatman, C. E. (2021). Barriers to the initiation of home modifications for older adults for fall prevention. Geriatric orthopaedic Surgery & Rehabilitation, 12, 21514593211002160. 11. Makizako, H., Kubozono, T., Kiyama, R., Takenaka, T., Kuwahata, S., Tabira, T., Kanoya, T., Horinouchi, K., Shimada, H., & Ohishi, M. (2019). Associations of social frailty with loss of muscle mass and muscle weakness among community-dwelling older adults. Geriatrics & Gerontology International, 19(1), 76–80. 12. Panini, L., & Cucchiara, R. (2003). A machine learning approach for human posture detection in domotics applications. In 12th International Conference on Image Analysis and Processing, 2003. Proceedings (pp. 103–108). IEEE. 13. Agrawal, Y., Shah, Y., & Sharma, A. (2020). implementation of machine learning technique for identification of yoga poses. In 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 40–43). IEEE. 14. Hussien, I. O., Dashtipour, K., & Hussain, A. (2018). Comparison of sentiment analysis approaches using modern Arabic and Sudanese Dialect. In International Conference on Brain Inspired Cognitive Systems (pp. 615–624). Springer, Cham.

Chapter 9

Case Study in Elderly Consultancy Services

9.1 Introduction The rise of the ageing population is a regional issue in a country and a global issue affecting the world [1, 2]. Associating with the increased ageing population, a higher rate of chronic diseases and physical deterioration among older people would also cause the demand for long-term care services to rise [3, 4]. As people age, they suffer from more and more illnesses. The healthcare system of every country will face a significant challenge to meet the demand. The long-term care system is one of the components that will be affected seriously [5–8]. In Taiwan, the launch of long-term care project 2.0 targets to provide various healthcare services, including care service, transportation service, nutrition meals, home nursing, community rehabilitation, respite care service and long-term care institutions to the citizen aged or above [9]. It aims to establish an accessible, affordable, universal long-term care service with good quality [10]. To provide a quality service, the role of care managers in Tier A of the ABC model, i.e. Integrated Community Service Centre, becomes more important to formulate the care solutions to the elderly according to their health situation. In addition, they act as the coordinator to link care services resources to perform the healthcare services with high quality [11]. However, the shortage of knowledgeable healthcare staff and equipment increases the challenges for care managers to assess massive health information and generate accurate care solutions quickly. Therefore, this chapter proposes an intelligent care planning system (ICPS) to facilitate care planning processes in the elderly consultancy service through the integration of artificial intelligence (AI) techniques. With the useful data from electronic health records, case-based reasoning (CBR) is used to classify the types of long-term care needed and formulate effective care solutions for the elderly by extracting the relevant knowledge from similar past care records. After that, the k-mean clustering technique is embedded in the CBR engine to evaluate the usefulness of care solutions generated to ensure the quality of the service. A case study is conducted in one of the integrated community service centres located in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_9

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Taiwan. The results show that the proposed ICPS helps improve the operation performance in care planning and hence increase their healthcare service satisfaction. The rest of the chapter is organized as follows: the research methodology of the proposed ICPS is presented in Sect. 9.2, while a case study is conducted in Sect. 9.3. Section 9.4 presents the result and discussion, and the conclusion is drawn in Sect. 9.5.

9.2 Research Methodology In this section, the intelligent care planning system (ICPS) is developed to facilitate the decision making in care planning which consists of three modules: (i) cloudbased data collection module, (ii) care solution generation module, and (iii) solution evaluation module. Figure 9.1 shows the architecture of the ICPS.

9.2.1 Cloud-Based Data Collection Module In the cloud-based data collection module, the cloud-based health records platform is used to collect elderly information. Typically, the elderly are required to provide and upload their personal information, such as name, gender and ages, historical health records, such as body check reports and test records, and medical records, such as blood pressure data, heart rate data and blood glucose level, to the cloud-based health records platform. At the same time, the in-depth interview will arrange to collect to understand the elderly behaviour and needs. Care managers will visit their homes to identify daily problems and assess the risk levels in their daily living. To ensure the quality of decision-making in the next two modules, i.e. case solution generation module and solution module, data preprocessing, including data cleaning, data integration, and data reduction, is required to remove incomplete and inconsistent data from the cloud database. Considering the various formats of historical health records uploaded by the elderly, such documents will be transformed into standard formats to facilitate further data analysis processes. Care managers must perform the data verification process to reduce the manual/typo mistakes inputted by the elderly in the online platform.

9.2.2 Care Solution Generation Module All relevant data are extracted from the cloud database and then transferred to the care solution generation module. This module is the core module in the proposed ICPS, which adopts the CBR technique. CBR uses knowledge from similar past health records to formulate the appropriate type of healthcare service, i.e. either home care service or nursing home service, and the care guidelines. Four steps are involved

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Fig. 9.1 Architecture of the ICPS

in the generic CBR, which are (i) case retrieval, (ii) case reuse, (iii) case revision and (iv) case retention [12, 13]. With the new input problem in this module, i.e. a new application for long-term healthcare services, the CBR engine will search and retrieve the most similar care records for solution generation. The searching path of the CBR is based on the construction of an indexing tree with various levels. Only a small group of past case records that fulfilled the specific requirements will be reminded at the bottom level of the tree. After that, the nearest neighbour method is used to rank the selected past care records according to their total similarity value, as shown in Eq. (9.1). Care managers must define the weighting of the attributes that may use to calculate the similarity value.

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n i=1

wi × sim(Cold , Cnew ) n i=1 wi

(9.1)

where wi is the weighting of predefined attributes and sim(C old , C new ) is the similarity value between the new inputted problem and the past care records. By ranking the total similarity value of selected past care records in descending order, care managers can easily distinguish the care records with the highest similarity value and treat them as the most similar care records to generate care solutions for the elderly. In the case revision step, considering individual elderly may face different problems in a reallife situation. There is a need to modify and revise the care solutions suggested from the selected past care records to meet their specific requirements. By integrating the new elements in the past care records, a new care plan with an appropriate type of healthcare services and care guidelines can be generated for satisfying the needs of the elderly. The evaluation process is added to ensure the quality of the new care plan before the case retention step. The detailed description of the evaluation process is discussed in Sect. 9.2.3. After the evaluation process, the high-quality care plan is then sent and stored in the case library, which carries all storage cases. The case library acts as a reference for future use in the CBR system.

9.2.3 Solution Evaluation Module This module aims to assess the quality of care solutions generated from the care solution generation module using the k-mean clustering technique. A questionnaire is firstly distributed to obtain the subjective data regarding the feedbacks of the care solutions from the elderly and their family members. In addition, a follow-up interview is conducted to collect the objective data. Care managers will ask questions about the quality of the services listed in the care plan. Apart from collecting the data from the elderly and their family, care managers will also interview the healthcare staff to understand their difficulties in delivering the healthcare services. Both subjective data and objective data can be collected to weigh and score the performance of the generated care solutions. After that, the k-mean clustering is used to classify the scored case into meaningful clusters, i.e. good cases cluster and bad cases cluster. Instead of using a fixed cut-off line, a fluctuating mid-point will be used as the cluster centre to classify the cases by considering the new cases inserted in the case library. Examples of care plan score clustering are shown in Tables 9.1 and 9.2. As a result, the care solution with good quality can be retained in the case library for further use.

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Table 9.1 Example of clustering before adding new care solution Score

Distance G

Distance B

Cluster

Mean

92

30

29

Good

74.7

81

19

18

Good

80

18

17

Good

77

15

14

Good

74

12

11

Good

71

9

8

Good

68

6

5

Good

66

4

3

Good

63

1

0

Good

62

0

1

Bad

62

0

1

Bad

59

3

4

Bad

58

4

5

Bad

52

10

11

Bad

51

11

12

Bad

50

12

13

Bad

49

13

14

Bad

55.4

9.3 Case Study To validate the feasibility of the proposed ICPS, a case study is conducted in one of the integrated community service centres. With more than 100 healthcare staff, including care managers, healthcare assistants, social workers, physiotherapists, nurses, and doctors, the case company aims to deliver better healthcare services to the elderly to maintain their quality of life and happiness in the future daily living. The target applicants of the case company are mainly the family member of an elderly aged 65 or above who is searching for appropriate nursing home or home care services. Each application is treated as an individual case to provide accurate healthcare services. A case manager will be responsible for assessing their needs based on the historical healthcare records and interviews. Currently, the process of healthcare consultancy services in the case company is: • The care manager will interview the elderly and their families to identify their needs and seek professional advice. At the same time, the care manager may suggest customers apply for government subsidies if they are eligible in an application. • The care manager will contact the corresponding nursing home or home care service centre from their detailed database, which shows the capability of such a healthcare organization.

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Table 9.2 Clustering results after adding new care solution Score

Distance G

Distance B

Cluster

Mean

92

39

17

Good

76.1

81

28

6

Good

80

27

5

Good

77

24

2

Good

76

23

1

Good

* 74

21

1

Good

71

18

4

Good

68

15

7

Good

66

13

9

Good

63

10

12

Bad

62

9

13

Bad

62

9

13

Bad

59

6

16

Bad

58

5

17

Bad

52

1

23

Bad

51

2

24

Bad

50

3

25

Bad

49

4

26

Bad

57.1

• The care manager will arrange a site visit with the applicant and clarify services provided by the nursing home or home care services. • After evaluating their financial situation, historical health records, service required, and feelings from the elderly, customers can decide after evaluating their financial situation. • The care manager will follow up the case one year after settlement for free to maintain the service quality and act as a bridge between families and the nursing home.

9.3.1 Problems in the Case Company However, two problems are found in existing processes of healthcare consultancy service in the case company, which are (i) lack of systematic approach to generate care solutions, and (ii) Unable to assess the quality of care solutions generated for the elderly. 1.

Lack of systematic approach to generating care solutions

Care managers in the case company have to evaluate the needs of the elderly to decide for the elderly manually. Based on the evaluation of massive data collected from

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interviews and historical health records, care manager will provide corresponding suggestions like residential care, community care, or home care to the applicant. After constructing the care solution plan, it will pass to professionals for further advice. However, concerning the shortage of knowledgeable care managers in the case company, it is hard for them to effectively formulate accurate and error-free care solutions. As a result, a systemic approach in healthcare service suggestions is needed. 2.

Unable to assess the quality of care solutions generated for the elderly

In the case company, the feedback from the elderly or the families is noted in files. Whether the result is good or bad, all the files are stored in the database or storage. When a new applicant received a care solution with poor quality may be used for solving the new problems. Moreover, this feedback did not reflect the actual situation of the elderly. For example, the elderly felt unhappy because of the personality mismatch with the health worker in that particular nursing home. Thus, it may cause misunderstanding to the new applicant. A detailed questionnaire is needed to record and reflect the service quality so that the new applicant can easily know the past cases’ details.

9.3.2 Implementation of the Proposed ICPS in the Case Company To address the mentioned issues, the proposed ICPS is implemented in the case company to facilitate the decision making in the care planning process. Figure 9.2 show the operation flow of the proposed ICPS. HTML, PHP and MySQL are used to construct the ICPS.

Fig. 9.2 Operation flow of the ICPS

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They are starting with a login page in the cloud-based online platform. Databases including user database, temporary database and case library are constructed to collect the information of the elderly, such as personal information (i.e. name, ID no., date of birth, age, contact, gender, address, contact person, their relationship, phone no. of contact person, source of care, living condition, personality), medical history (i.e. the percentage of fall, arthritis, cancer, dysphagia, liver disease, mental health, urinary) and body condition (i.e. weight, height, blood glucose, heart rate, blood pressure, body temperature, drinking habit, smoking habit). In addition, the care manager will also input the interview results to the cloud-based online platform. All data will first be stored in the temporary database for the further calculation of the CBR. After that, the CBR technique provides care solutions for the elderly in the “retrieve.php”. According to Tang et al. [14, 15], five questions constructed as a decision tree structure are asked to retrieve the past care records from the case library, which are (i) type of mobility, (ii) level of self-care ability, (iii) type of neuropsychiatric condition, (iv) communication method and (v) age. Figure 9.3 shows the decision tree structure for case retrieval. The retrieved small group of past care records are then transferred to the “reuse.php”. By taking the reference from Siu et al. [3], 17 attributes such as living condition, blood pressure level, mental status, smoking behaviour are defined to compute the total similarity value to the new application using Eq. (9.1). Care managers are also required to define the weighting of an individual attribute that may affect the healthcare services delivered. A new applicant

Fig. 9.3 Decision tree structure for case retrieval

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from the case company is inputted and processed to illustrate the mechanism of CBR in the proposed ICPS, as shown in Table 9.3. The user interfaces for case reuse in the proposed ICPS is shown in Fig. 9.4. After ranking the case records in descending order, the part care record with the highest similarity value can be easily identified. The results show that the past care record ID 33 has the highest similarity value, which is treated as the most significant case for a further case revision. With the past care record as a reference, the care manager can extract useful information to assign the type of the long-term care service and generate the care guidelines for the healthcare staff. The modification and editing of the care solutions are needed to deliver the appropriate healthcare services by considering the specific needs of the individual elderly. In this case, the nursing home services will be assigned for the new applicant, and the content of the care plan will also transfer to the corresponding nursing home to follow. Apart from generating long-term care service, the daily caring guideline will also provide to the nursing home to allocate the healthcare resources such as workforce and equipment for serving the elderly. For example, the care plan will list how often the elderly are required to take medicine and the types of biometric data that need to be monitored. Before retaining the new care solution, the evaluation for the performance of the care solution is executed to ensure that only the care plan with high quality will Table 9.3 Information of the new applicant No

Information

No

Information

Name

Chan Sui Ho

Body temperature

36.7

Gender

Male

Blood pressure

120/86 mmHg

Age

65

Heart rate

86 bpm

Education level

Primary school

Mobility

Need the assistive devices

Height

176 cm

Self-care ability

Need the assistive devices

Weight

50 kg

Communication

Oral

Fig. 9.4 User interface for case reuse

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Fig. 9.5 Interface of the database for storing care plans with high quality

be reused next time. In this case, k-mean clustering is employed. A questionnaire designed by the case company is distributed to both healthcare staff in the nursing home and the elderly and their family to collect subjective data. Regarding the objective data, an interview is conducted to review the performance of the care solutions. Such data are stored in the “feedback.php”, and the score would be calculated and sent to “K-means.php” for clustering using Eq. (9.2). The function of K-means clustering is to classify good cases and bad cases. Thus, the number of clusters is 2, i.e. k = 2, and the mean of each cluster is also calculated. The random number is assigned at the initial mean of the two clusters. One is between 1 to 50, and another one is between 51–100.   Di j = xi − c j 

(9.2)

where Dij is the distance, x i is the score of case i, cj is the mean of cluster j. If the Di1 smaller than Di2 , then that x i belongs to c1 and vice versa. If the new care solution is classified as a good case, it will be transferred and retained in a “good case database” and become the source for the next CBR cyclical processes. Figure 9.5 show the interface of a good case database.

9.4 Results and Discussion The ICPS was implemented in the integrated community service centres in helping care managers to formulate care solutions. After implementing ICPS, it is found

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Table 9.4 Improvement in the performance of care planning Before implementing ICPS

After Implementing ICPS

Improvement

Time for care solution formulation 1. Evaluate the needs of the elderly based on historical health information (mins)

60

5

92%

2. Select the types of long-term care services and generate the care solutions (mins)

60

30

50%

Total

120

35

71%

that the case company improves the performance in the following items: (1) efficiency and effectiveness of care plan formulation, (2) feedback collection, and (3) service satisfaction. In this section, the results of EKMS are discussed to prove its contribution to the company. The system performance among traditional case-based reasoning modules and the proposed ICPS.

9.4.1 Improvement in the Efficiency of Care Plan Formulation The measure of time is recorded by the average time that groups of care managers spend on creating new care plans for the elderly. Table 9.4 shows the results of the time required for the tasks of care planning before and after implementing the ICPS. With the CBR approach, care managers can take past cases with a similar situation to the current elderly for reference without searching documents physically. The time spends on reviewing the relevant health record is reduced from 60 to 5 min which is improved by 92%. Also, time spends in formulating care plan detail is also improved by 50%.

9.4.2 Performance in Feedback Collection As mentioned before, the case company will provide a one-year follow-up service to check the suitability of healthcare services delivered. Also, the reflections from the elderly would be assessed. If any problem exceeds, the staff will make corresponding modifications due to commends from the elderly. However, the duration of case follow-up is long, which is not suitable to apply in the long-term care system. With the implementation of ICPS, care managers can modify results easily and come up with a

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Table 9.5 Improvement in service satisfaction Before implementing ICPS

After implementing ICPS

Improvement (%)

Feedback scores

76.8

86.4

12.5

Part A—General service

72

85

18.1

Part B—Residential care / Home care

75

92

22.7

score to perform a continuous quality inspection. Tables 9.5 shows the improvement in the performance in feedback collection. With the help of the proposed system, the care managers can input the key attributes from the system and search the elderly feedbacks. Therefore, the time for collecting such feedback can be shortened from 60 to 10 min.

9.4.3 Improvement in Service Satisfaction The service satisfaction result is reflected in the questionnaire results in the care solution evaluation module of the proposed ICPS. The satisfaction of care plan formulation is measured by comparing the feedback score before and after implementing the ICPS. Table 9.5 shows the difference in mean scores from the care plans. The feedback questionnaire contains two parts, (A) General service quality and (B) Residential care/ Home care service quality. Questions in part A focus on the service quality delivered during plan formulation while part B focuses on the service quality of particular care assigned after planning. From Table 9.5, the mean scores in part A are 72 without the ICPS, and 78 with ICPS, which is improved by 12.5%. The reason is the shortened time that the elderly wait for the care plan formulation. Mean scores in part B are 75 without ICPS and 92 with ICPS, which is improved by 22.7%. Appropriate healthcare service can be delivered to the elderly in a cost-effective and fast-responsive manner by knowing the past care records with high quality. Therefore, the overall feedback scores are 76.8 without ICPS and 86.4 with ICPS. The total service satisfaction of care planning in the case company can be improved.

9.5 Conclusion As the ageing population is expected to be increased in the coming future, the demand for long-term care services would also increase. The existing healthcare system in every country faces a significant challenge to meet the increasing demands of such healthcare services. Residential care services and home care services are typically

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healthcare services that provide long-term care to the elderly in the community. However, the shortage of healthcare resources, especially knowledgeable healthcare staff in delivering the healthcare services, further increases the pressure of healthcare organizations to provide quality care. Meanwhile, the working population would also decrease when the ageing population rise. Thus, the healthcare staff will face a shortage in the long run. In Taiwan, the launch of a long-term care plan aims to solve the resources allocation problem in providing long-term service within a community. ABC model is constructed in a comprehensive community service system in LTCP 2.0. This chapter focuses on tier A in the ABC model about the determination of benefits level and care plan at the community centre by the care manager. The intelligent care planning system (ICPS) is designed to facilitate the decision making in care planning for the elderly consultancy service. The integration of CBR and K-meaning clustering allows the formulation of care plans on time and improves the quality of healthcare services. Through the case study, it is found that the implementation of ICPS can boost the performance of care planning regarding (1) efficiency and effectiveness of care plan formulation, (2) feedback collection, and (3) service satisfaction. As the importance of care planning in LTCP, future research can focus on adopting text mining techniques to effectively extract updated healthcare knowledge from the internet to improve the quality of revising past care plans.

References 1. Feng, Z. (2019). Global convergence: Aging and long-term care policy challenges in the developing world. Journal of Aging & Social Policy, 31(4), 291–297. 2. Kaplan, M. A., & Inguanzo, M. M. (2017). The social, economic, and public health consequences of global population aging: Implications for social work practice and public policy. Journal of Social Work in the Global Community, 2(1), 1–12. 3. Galiana, J., & Haseltine, W. A. (2019). Healthcare in the United States. In Aging Well (pp. 7–18). Palgrave Macmillan, Singapore. 4. Colombo, B., Antonietti, A., & Daneau, B. (2018). The relationships between cognitive reserve and creativity. A study on American aging population. Frontiers in psychology, 9, 764. 5. Storms, H., Marquet, K., Aertgeerts, B., & Claes, N. (2017). Prevalence of inappropriate medication use in residential long-term care facilities for the elderly: A systematic review. European Journal of General Practice, 23(1), 69–77. 6. Kyaw, K. T. (2019, November). Prediction of Long-term Care Utilization by Functional Status in the United States Aging population between 2008 to 2016. In APHA’s 2019 Annual Meeting and Expo (Nov. 2-Nov. 6). American Public Health Association. 7. D’Adamo, H., Yoshikawa, T., & Ouslander, J. G. (2020). Coronavirus disease 2019 in geriatrics and long-term care: The ABCDs of COVID-19. Journal of the American Geriatrics Society, 68(5), 912–917. 8. Murphy, A., Kowal, P., Albertini, M., Rechel, B., Chatterji, S., & Hanson, K. (2018). Family transfers and long-term care: An analysis of the WHO Study on global AGEing and adult health (SAGE). The Journal of the Economics of Ageing, 12, 195–201. 9. Yeh, M. J. (2020). Long-term care system in Taiwan: The 2017 major reform and its challenges. Ageing & Society, 40(6), 1334–1351. 10. Hsu, H. C., & Chen, C. F. (2019). LTC 2.0: The 2017 reform of home-and community-based long-term care in Taiwan. Health Policy, 123(10), 912–916.

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11. Chen, S. H., Pai, F. Y., & Yeh, T. M. (2020). Using the importance-satisfaction model and service quality performance matrix to improve long-term care service quality in Taiwan. Applied Sciences, 10(1), 85. 12. Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI communications, 7(1), 39–59. 13. Kolodner, J. (2014). Case-based reasoning. Morgan Kaufmann. 14. Tang, V., Choy, K. L., Ho, G. T. S., Lam, H. Y., & Tsang, Y. P. (2019). An IoMT-based geriatric care management system for achieving smart health in nursing homes. Industrial Management & Data Systems. 15. Tang, V., Siu, P. K. Y., Choy, K. L., Lam, H. Y., Ho, G. T. S., Lee, C. K. M., & Tsang, Y. P. (2019). An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry. Expert Systems, 36(2), e12369.

Chapter 10

Case Study in Remote Diagnosis

10.1 Introduction In recent years, the concept of smart health, which integrates the idea of the Internet of Things (IoT) and artificial intelligence (AI), has been deployed to support the way of healthcare services delivery and equipment involved in the community [1, 2]. Smart health offers the benefits of facilitating information sharing among healthcare stakeholders, supporting the decision making in health prediction and diseases diagnosis analysis and accelerating the efficiency of the operation of daily activities [3]. For example, Norton [4] applied an effective nursing management system using information technology to relieve staff’s workload and enhance the operation of care planning for patients. The adoption of IT also increases the accuracy and speed of patient diagnosis to achieve cost-effective operation. In addition, Mshali et al. [5] proposed a health monitoring system using IoT technology for monitoring the daily activities and behaviour of the elderly in their home environment. With the rapid development of advanced technologies, researchers have started to apply advanced technologies in healthcare since 1990 to increase collaboration across the healthcare value supply chain. According to the World Health Organisation (WHO) [6], eHealth defines as the use of information communication technologies (ICT) for health to maintain the health outcomes of the patients. In recent decades, it was found that there is a shift of the healthcare model from centralised and non-integrated care services to customised patient care services, as shown in Fig. 10.1. Instead of providing standardised healthcare services, patients expect to receive holistic healthcare services offered by various formal and informal care organisations such as hospitals, elderly homes and nursing homes. Therefore, an effective information management system is vital for these healthcare organisations to share patient health information and allocate resources regarding staffing and equipment. The applications of eHealth have rapidly grown in recent years. This growth achieves a wide variety of goals, including reducing healthcare costs, increasing the information access to healthcare, enhancing healthcare staff’s service capacity © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6_10

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Fig. 10.1 Shift of healthcare models

and effectiveness, streamlining service delivery, and facilitating self-management and participation from the patients and their families [7–9]. Figure 10.2 shows the evolution of the eHealth application. Doing so helps reduce the workload and pressure of healthcare staff and hence relieve the problems of the limitations of healthcare resources [10, 11]. Due to the increasing use of mobile applications, the new field of mHealth was proposed to address healthcare issues. Through the support of mobile devices, mHealth can help support daily health practices. According to the statistics provided by the International Telecommunication Union [12], there are more than 3.6 billion Internet users in 2017 and 2.6 billion users are from developing countries. This trend drives the transformation of healthcare service delivery through eHealth. Because of the potential of mHealth, research studies have been drawn in this area.

Fig. 10.2 Evolution of eHealth applications

10.2 IoT Based Healthcare Platform

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10.2 IoT Based Healthcare Platform Considering the benefits of IoT offering a digital platform for centralising data and allowing users and healthcare professionals to remote access, IoT in the healthcare industry is feasible to facilitate the information exchange among various healthcare parties [13–15]. In this section, an IoT-based healthcare model is proposed, which involved three layers, i.e. sensing layer, network layer and application, for achieving the functionalities of remote diagnosis and healthcare information management. Figure 10.3 shows the architecture of the prosper IoT-based healthcare model. Through the connection of diagnostic equipment in the sensing layer, healthcare data can be collected and then processed for data manipulation and knowledge management. Details of each layer are presented and discussed as follows.

Fig. 10.3 Architecture of the prosper IoT-based healthcare model

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Sensing Layer This layer is a foundation layer for the other higher layers in the IoT architecture. Various types of wireless sensors, such as pulse oximetry, blood pressure sensor, respiratory flow sensor and electrocardiograph (ECG) sensor, are equipped in healthcare organisations. These organisations provide long-term care services for the elderly. Healthcare organisations, including township hospitals, elderly homes, community health service centres, countryside village clinics and institutions for disease control and prevention, are the main research targets for implementing the proposed IoT platform. IoT sensors can facilitate the collection of vital signs, including body temperature, pulse, oxygen saturation level and blood pressure, which are essential data for indicating the health status of the body functions. On the other hand, healthcare professionals can use such data to assess the general physical health of the elderly for achieving real-time health monitoring. Appropriate health diagnosis and treatment plans formulated by doctors heavily rely on collecting accurate vital data for health measurement and processing. Sensing nodes with sensors, embedded processors, and wireless transceiver modules are the smallest units in the sensing layer to obtain, synchronise, and transmit the digital signals from sensors to the proposed platform. Furthermore, wireless communication technologies such as Wi-Fi, ZigBee, Bluetooth and mobile network are used as the communication channels for data transmission. By registering the AppIDs for various sensors, health data can be gathered and stored under various network domains. Network Layer The network layer is the interconnection layer between the sensing layer and application. This layer aims to provide an efficient, stable and secure environment for data transmission. Vital sign signals and other health data from the sensing layer can be gathered and stored in the cloud platform for further data analytics processes. Healthcare professionals can perform real-time health monitoring and medical diagnostics through the reviewing of the health records of the elderly instantly and securely. In addition, this platform also connects to mobile devices such as mobile phones so that users can perform self-diagnosis and self-management. To increase flexibility, a wide range of communication technologies can connect the proposed platform, including Wi-Fi, Bluetooth and ZigBee. Considering the research targets of long-term carafe services providers in this study, the selection of communication technologies depends on the application and the ease of use. For example, in the elderly home, the costs for deploying this IoT platform is the most concern for the elderly and their families. In contrast, professional medical measurement equipment is equipped in hospitals for providing reliable services. Table 10.1 shows the comparison between different communication technologies. Firstly, Wi-Fi is a technology that can connect with a wide range of digital devices such as personal computers, PDA, mobile phones and other terminals devices. Besides the wired connection, Wi-Fi also offers wireless network communication, which aims to improve the interoperability of wireless networks based on IEEE802.11 standards. It can provide convenience for mobile office users without the use of cabling for the connection. Secondly, with the duplex

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Table 10.1 Comparison between wireless LAN, Bluetooth and Zigbee WIFI

Bluetooth

ZigBee

IEEE specification

802.11 a/b/g

802.15.1

802.15.4

Max. signal rate

11 Mb/s

1 Mb/s

250 kb/s

Data type

Video, audio, graphics, pictures, files

Audio, graphics, pictures, files

Small data packet

Battery life

Hours

1 week

>1 year

Nodes per master

32

7

64 000

Range (m)

100

10

70

Complexity

Complex

Very complex

Simple

communication mode, Bluetooth enables visual presentation and analysis in systems. The use of Bluetooth can simplify communication and data transmission between a mobile device and sensing devices. By doing so, users can quickly and efficiently gather and diagnose the collected data. ZigBee has relatively low power consumption and cost in data transmission and communication among the sensing devices compared with the mentioned two technologies. Application Layer The application layer is a top layer in the proposed platform for achieving healthcare management and information sharing, and collaboration among various healthcare parties and systems. The proposed platform provides the knowledge to support various long-term care services such as diseases control and prediction, remote health monitoring, and electronic health records through big data analytics. Significantly, this platform offers benefits to small and rural hospitals with few healthcare resources. Experts such as doctors and social workers can utilise the knowledge from the proposed system to improve the medical diagnosis’s quality to maximise the quality of healthcare services. Eventually, this platform can promote cooperation among various healthcare parties and the concept of community care through home-based self-diagnosis. Regarding healthcare management and knowledge manipulation functions, the proposed platform enables knowledge exchange in both explicit and tacit aspects for medical diagnosis. Health data such as vital signs and other measure data collected by sensors is gathered and transmitted to the cloud and then analysed by big data analytics and deep learning techniques. Through the computation processes, helpful knowledge can be generated and extracted for facilitating the decision supports in the delivery of appropriate and fast-responsiveness healthcare services. Knowledgemining from massive health data is an application of big data analytics and deep learning techniques. It is essential for turning the doctors’ rich and tacit diagnosis, treatment experience and knowledge into explicit visual diagnosis information, enabling them to share, learn, reuse and even innovate. Figure 10.4 present the flow of knowledge building.

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Fig. 10.4 The flow of knowledge building

As the core function in the proposed platform, two sets of data sources are entered as the inputs for knowledge management, which are (i) doctor diagnosis and treatment information dataset (implicit knowledge), and (ii) health data collected by the sensors in sensing layer. Using the Hadoop Distributed File System (HDFS) distributed storage system, these two sets of data can be stored in the healthcare cloud. In the HDFS distributed storage system, each storage node is applied for storing the actual data blocks and then configured for different applications according to its needs. To improve efficiency in dealing with dramatically increasing amounts of data in HDFS, research studies have focused on investigating storage optimisation by considering the hardware reliability and response time for file reading. Furthermore, the benefits offered by deep learning techniques facilitate its adoption in the healthcare industry for health monitoring, clinical prediction and remote diagnosis [16–18]. By mining the collected, hidden relationship and knowledge can be identified and discovered to generate customised care solutions so that the medical knowledge warehouses can be established to facilitate clinical decision-making, information sharing, and knowledge manipulation. Data processing is needed to improve the quality of healthcare solutions generated from the proposed platform.

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10.3 Case Study In this section, the proposed platform was applied in China for establishing the WeChat Mini Program for the trial purpose. The reasons for selecting China as the research samples are the high penetration rate of internet users and the nearly full mobile broadband coverage [19]. It is believed that mobile healthcare (mHealth) solutions provide an alternative for promoting essential public health and improve the overall quality of healthcare services in China. As the report announced by the China Internet Network Information Centre in 2018, instead of the use the personal computer, mHealth may bring a more significant real-life implication than eHealth. Users can use mobile phones to access healthcare information through the IoT-based platform. Regarding the WeChat Mini Program developed based on the proposed IoT healthcare platform, WeChat is one of the popular social messaging apps with the most significant users bases in China. The second quarter of 2017 (Statista, 2018) has around 963 million monthly active users. With the rapid development of advanced technology, WeChat has evolved from only messaging functions to “China’s app for everything”, which has a strong network connection with different digital platforms. Therefore, there are many functionalities of WeChat, such as billing function, social networking, online shopping and ordering in a restaurant. According to Digital Initiative Digest (2017), WeChat in China is “a single app for Facebook, WhatsApp, Messenger, Venmo, Grubhub, Amazon, Uber, Apple Pay and many other such services in the West”. In the era of digitalisation, WeChat provides the support for Chinese to change their lifestyle from the physical world to the mobile world. Furthermore, the opened-up platform offered by WeChat allows the third-party developers to design their functions in the WeChat platform. Users can quickly assess the small application anywhere and anytime without downloading the extra app. The WeChat Mini Program is a perfect starting point for developing mHealth solutions for both Android and IOS users. In addition, the low investment cost is another attractive point for the users to develop the native app in WeChat, which can reduce the development cost by 80%. From the user perspective, they are more willing to assess the mHealth in WeChat since they can bookmark the WeChat Mini Program and visit the contact easily without consuming the phone’s storage. Considering the above benefits offered by the WeChat Mini Program, the proposed mHealth solution will be launched with three core functions: remote medical diagnosis, healthcare knowledge consultation, and self-healthcare management. Figures 10.5, 10.6 and 10.7 shows the examples of the WeChat Mini Program for mHealth. Under the IoT environment, health data can be real-time collected, transmitted and stored in the cloud database through wireless communication technologies. Such data are then analysed using big data analytics to support decision makings of doctors and healthcare professionals in medical diagnosis and healthcare plan generation. In addition, through the WeChat platform, various healthcare organisation parties can efficiently exchange information which facilitates their cooperation. Also, it can encourage the self-healthcare management in a community in which users with

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Fig. 10.5 An example for remote medical diagnosis

Fig. 10.6 An example for healthcare knowledge consultation

chronic disease can regularly update their health status in WeChat. Figure 10.8 shows the data flow for self-healthcare management. With good interaction with doctors and healthcare professionals, fast, responsive and accurate healthcare services can be delivered to enhance the overall service satisfaction.

10.3 Case Study Fig. 10.7 An example for self-healthcare management

Fig. 10.8 Data flow for self-healthcare management

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10.4 Discussion The rapid development of advanced technologies changes the lifestyle of people from the physical world to the digital world. The adoption of mobile technology has been widespread in China and WeChat for social networking and information searching functions. This study proposes an IoT-based healthcare platform to (i) collect health data such as vital signs and body functioning data through the sensing devices and (ii) facilitate medical diagnosis in healthcare organisations. From the managerial perspective, the use of the proposed system facilitates resource allocation in formulating healthcare solutions by using the data collected from IoT. This system improves the efficiency, traceability, and transparency among healthcare parties and enhances their communication. Through the seamless connection between sensing devices and backend cloud databases, the functions of medical diagnosis and self-healthcare management can be generated with the aid of big data analytics. Considering that healthcare resources are limited, knowledge retained in the IoT-based healthcare platform can help achieve the operation excellence in healthcare organisations to maintain positive outcomes for the elderly. In the view of the elderly and their families, the proposed mHealth solution in WeChat can promote community care in daily living. Instead of the traditional regular visiting healthcare organisation for health checking and monitoring, users can use their mobile phone to review their health status. In addition, useful healthcare knowledge can be effectively delivered to the elderly through WeChat to enhance self-healthcare management. The evolution of technologies from eHealth, mHealth to smart health brings significant contributions to the healthcare industry, which move it a step towards the virtualisation of hardware, software, platform solutions, and finally achieving virtual healthcare delivery in the community. This changes the ways for caregivers to deliver long-term care services, starting from health monitoring, health assessment and care planning.

10.5 Conclusion This study proposes the IoT-based healthcare platform for remote health monitoring through sensing devices and the support of disease diagnosis. The proposed platform consists of three layers: sensing layer, network layer and application layer. Health data collected from the sensing devices such as respiratory flow sensors and blood pressure sensors are stored in the cloud platform through wireless communication technologies. After that, the functionalities of remote diagnosis, disease control and analysis, mobile measuring and collaboration tasks can be performed through big data analytics. In the case study, the mobile healthcare solution was constructed based on the structure of the proposed IoT-based healthcare platform using the WeChat Mini Program. The work results provide the technical basis for the development of smart health solutions, which provides directions for the research in the field of the adoption

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of advancement of healthcare systems for long-term care services. In addition, this study can be treated as the foundation for applying remote monitoring and diagnosis in the healthcare industry. Considering the sensitivity of the health information of millions of patients, future research should focus on improving security and data privacy on various healthcare platforms/systems.

References 1. Sood, S. K., & Mahajan, I. (2017). Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus. Computers in Industry, 91, 33–44. 2. Holzinger, A., Röcker, C., & Ziefle, M. (2015). From smart health to smart hospitals. In Smart Health (pp. 1–20). Springer, Cham. 3. Shen, X. L., Li, Y. J., & Sun, Y. (2018). Wearable health information systems intermittent discontinuance: A revised expectation-disconfirmation model. Industrial Management & Data Systems., 118(3), 506–523. 4. Norten, A. (2011). Nurses’ acceptance of RFID technology in a mandatory-use environment. Doctoral dissertation. Nova Southeastern University. Retrieved from: https://nsuworks.nova. edu/gscis_etd/263/. 5. Mshali, H., Lemlouma, T., & Magoni, D. (2018). Adaptive monitoring system for e-health smart homes. Pervasive and Mobile Computing, 43, 1–19. 6. World Health Organization. (2018). eHealth. Retrieved from. 7. Kreps, G. L., & Neuhauser, L. (2010). New directions in eHealth communication: Opportunities and challenges. Patient education and Counseling, 78(3), 329–336. 8. Boogerd, E. A., Arts, T., Engelen, L. J., & van De Belt, T. H. (2015). “What is eHealth”: time for an update?. JMIR Research Protocols, 4(1), e29. 9. Penedo, F. J., Oswald, L. B., Kronenfeld, J. P., Garcia, S. F., Cella, D., & Yanez, B. (2020). The increasing value of eHealth in the delivery of patient-centred cancer care. The Lancet Oncology, 21(5), e240–e251. 10. Buchanan, W. J., Fan, L., Ekonomou, E., Lo, O., & Thuemmler, C. (2012). Case Study: Moving Towards an e-health Platform to Store NHS Patient Information in the Cloud. Paper presented at Cloud Computing in the Public Sector: The Way Forward, London. 11. Tahir, A., Chen, F., Khan, H. U., Ming, Z., Ahmad, A., Nazir, S., & Shafiq, M. (2020). A systematic review on cloud storage mechanisms concerning e-healthcare systems. Sensors, 20(18), 5392. 12. International Telecommunication Union. (2017). ICT Facts and Figures 2017. Retrieved from: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx. 13. Kodali, R. K., Swamy, G., & Lakshmi, B. (2015, December). An implementation of IoT for healthcare. In 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (pp. 411–416). IEEE. 14. Babu, B. S., Srikanth, K., Ramanjaneyulu, T., & Narayana, I. L. (2016). IoT for healthcare. International Journal of Science and Research, 5(2), 322–326. 15. Selvaraj, S., & Sundaravaradhan, S. (2020). Challenges and opportunities in IoT healthcare systems: A systematic review. SN Applied Sciences, 2(1), 1–8. 16. Loh, B. C. S., & Then, P. H. H. (2017). Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions. mHealth, 3, 45–54. 17. Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications, 105, 233–261. 18. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., & Dean, J. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(18). 19. South China Morning Post. (2016). China nears full mobile broadband coverage on back of increased 4G adoption. Retrieved from: http://www.scmp.com/tech/china-tech/article/199 1425/china-nears-full-mobile-broadband-coverage-back-increased-4g.

Conclusions and Future Directions of Research

Although healthcare is identified as one of the essential parts of human life, the rapid ageing population and related rising demands for resources worldwide are placing significant stress on the whole healthcare industry. This stress affects both the shortterm and long-term care service providers, which affect the quality of care (QoC) delivered to the elderly. The short-term care service providers provide emergency medical care, trauma care, disease diagnosis and treatment, and surgery. The longterm care service providers provide all kinds of personal care services (such as meal preparation, bathing, and laundry) and nursing services (such as convalescence, rehabilitation, and physiotherapy) to the elderly. With limited healthcare resources, there is a need to adopt advanced technologies such as IoT, cloud computing, big data analytics, and AI for achieving smart health to deliver timely and accurate healthcare services to the elderly. Firstly, the complementary nature of the IoT and cloud in terms of accessibility, security, data processing, storage and service sharing offers benefits for various areas such as health monitoring, health data management, diseases diagnosis. There are five layers in the IoT infrastructure: the IoT/IoMT devices in sensing layer, edge devices in pre-processing layer, cluster/fog processing layer, data storage in the persistence layer, and the top application layer. Through data integration, such a large volume of health data can be standardized for data analytics purposes using different AI techniques. This integration offers benefits to the healthcare organization, especially the long-term care services providers. From the case studies mentioned in the previous chapters, the adoption of AI can (i) help domestic homecare service planning and reduce the overtime working of caregivers, (ii) enhance data sharing and communication among various healthcare stakeholders and fall prevention, (iii) provide better health monitoring and prediction, and (iv) achieve functionalities of remote diagnosis, disease control and analysis. However, there are some challenges in adopting advanced technologies, including reliability, data security and privacy issues, the requirements in defining the settings, and sensitivity testing of the advanced techniques. For example, errors in the data may © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6

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occur and affect the reliability of the analysis and predictions. Some popular wearable devices appear to have significant variations of accuracy when collecting data from people. In addition, considering the high sensitivity characteristic of the health data, data security is always the primary concern in healthcare organizations regarding “how to guarantee data authenticity and confidentiality”. Therefore, it is worthy for both academic and industrial practitioners to focus on the above challenging areas. This direction can open up more research opportunities to further improve the efficiency, effectiveness, reliability of the healthcare systems for the elderly through real-time monitoring, detection and supports.

Acronyms and Glossary

AAL

Ambient Assisted Living

AD

Alzheimer’s Disease

ADLs

Activities of Daily Living

AHP

Analytic Hierarchy Process

A.I

Artificial Intelligence

API

Application Programming Interface

AR

Augmented Reality

BOA

Bisector of Area

CBR

Case-based Reasoning

CCS

Community Care Services

CDMA

Code Division Multiple Access

COG

Centre of Gravity

CPU

Central Processing Unit

DHSPS

Domestic Homecare Service Planning System

DICOM

Digital Imaging and Communications in Medicine

DNA

Deoxyribonucleic Acid

ECG

Electrocardiogram

EHealth

Electronic Health

EKMS

Electronic Key Management System

ESPP

Elderly Services Programme Plan

ETL

Extract, Transform, Load (continued)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. H. Wu et al., IoT for Elderly, Aging and eHealth, Lecture Notes on Data Engineering and Communications Technologies 108, https://doi.org/10.1007/978-3-030-93387-6

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Acronyms and Glossary

(continued) GA

Genetic Algorithm

GB

Gigabyte

GDP

Gross Domestic Product

GER

Gross Enrolment Ratio

GPRS

General Packet Radio Service

GSM

Global Systems for Mobile

HBase

Hadoop Database

HDFS

Hadoop Distributed File System

HIS

Health Information System

HIMSS

Healthcare Information and Management Systems Society

HTM

Hierarchical Temporal Memory

HTML

Hypertext Markup Language

Hz

Hertz

IADL

Instrumental Activities of Daily Living

IAL

Independent Assisted Living

ICPS

Intelligent Care Planning System

ICT

Information and Communication Technologies

IEEE

Institute of Electrical and Electronic Engineers

IIHS

IoT-based Intelligent Health System

IOS

iPhone Operating System

IoT

Internet of Things

IoMT

Internet of Medical Things

IT

Information Technology

K-d tree

K-dimensional Tree

KNN

K-nearest Neighbour

LANs

Local Area Network

LM

Leftmost Maximum

LTC

Long-term Care

LTCP 2.0

Long-term Care Plan 2.0

MANs

Metropolitan Area Network

MHealth

Mobile Health

MOM

Mean of Maxima

MQTT

Message Queuing Telemetry Transport

MySQL

My Structured Query Language

M2M

Machine-to-machine

NoSQL

Non-structured Query Language

OS

Operating System

PACE

All-inclusive Care for Elderly Program (continued)

Acronyms and Glossary

129

(continued) PDA

Personal Digital Assistant

PHP

Hypertext Pre-processor

QFD

Quality Function Deployment

QoC

Quality of Care

QoL

Quality of Life

RCS

Residential Care Services

RDF

Resource Description Framework

RM

Rightmost Maximum

SaaS

Software as a Service

TB

Terabyte

VPN

Virtual Private Network

WANs

Wide Area Network

WAP

Wireless Application Protocol

WHA

World Health Assembly

WHO

World Health Organisation

YARN

Yet Another Resource Negotiator