Supportive Smart Homes: Their Role in Aging in Place (Synthesis Lectures on Technology and Health) [1st ed. 2024] 3031373367, 9783031373367

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Synthesis Lectures on Technology and Health

Frank Knoefel · Bruce Wallace · Neil Thomas · Heidi Sveistrup · Rafik Goubran · Christine L. Laurin

Supportive Smart Homes Their Role in Aging in Place

Synthesis Lectures on Technology and Health Series Editors Ron Baecker, University of Toronto, Toronto, ON, Canada Andrew Sixsmith, Simon Fraser University, Vancouver, BC, Canada Sumi Helal, University of Florida, Gainesville, FL, USA Gillian R. Hayes, University of California, Irvine, CA, USA

The series publishes state-of-the-art short books on transformative technologies for health, wellness, and independent living. Our scope of publishing in the expanding health tech field includes: ● ● ● ●

Technology in support of active and healthy living and aging Digital technologies for health- and social-care improvement Diagnostic, screening, and tracking tools Assistive and rehabilitative technologies

The series includes a subseries of books published in partnership with Canada’s AGEWELL NCE that specifically addresses their 8 AgeTech Challenge Areas. Each lecture introduces the context in which the technology is used—wellness, health, medicine, special needs, or other contexts. Authors present and explain the technology and review promising applications and opportunities as well as limitations and challenges. They include material on their own work while surveying the broader landscape of related research, development, and impact.

Frank Knoefel · Bruce Wallace · Neil Thomas · Heidi Sveistrup · Rafik Goubran · Christine L. Laurin

Supportive Smart Homes Their Role in Aging in Place

Frank Knoefel Bruyère Research Institute Ottawa, ON, Canada

Bruce Wallace Carleton University Ottawa, ON, Canada

Neil Thomas University of Ottawa Ottawa, ON, Canada

Heidi Sveistrup University of Ottawa Ottawa, ON, Canada

Rafik Goubran Carleton University Ottawa, ON, Canada

Christine L. Laurin Bruyère Continuing Care Ottawa, ON, Canada

ISSN 2771-7054 ISSN 2771-7070 (electronic) Synthesis Lectures on Technology and Health ISBN 978-3-031-37336-7 ISBN 978-3-031-37337-4 (eBook) https://doi.org/10.1007/978-3-031-37337-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 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

Acknowledgements

The authors would like to thank the Bruyère Research Institute, Carleton University, and AGE-WELL Network of Centres of Excellence for supporting the AGE-WELL SAM3 National Innovation Hub. In many ways, the work at the hub has led to the writing of this book. We gratefully acknowledge the older Canadians and their families who have participated in our research and inspire us each day. Thank you to our research colleagues, in many disciplines and various institutions, who we have had the good fortune to collaborate with. We express sincere appreciation to our students in engineering, industrial design, and health, who have contributed to our projects, as well as the steady efforts of our research coordinators, research assistants, and lab technicians. We acknowledge funding organizations who have supported our work over the years, including AGE-WELL, NSERC, CIHR, NRC Aging in Place program, Mitacs, OBIO, OCI, CABHI, and the Innovation Fund of the Alternative Funding Plan for the Academic Health Sciences Centres of Ontario. We acknowledge AGE-WELL for the financial support of this book. Thank you to the entire AGE-WELL community: Network Management Office, researchers and trainees, older adults and caregivers, industry, government, community, and healthcare partners for your support and collaborative spirit. Their passion for innovation was essential in developing and sustaining this book’s creation. Frank Knoefel acknowledges funding for the University of Ottawa Brain and Mind—Bruyère Research Institute Chair in Primary Health Care Dementia Research. The authors would like to express their appreciation to Myriam Hamza for her research assistance and her creative artistry, especially her drawings which depict the personae discussed in the book. Thank you to Juliet Neun-Hornick, AGE-WELL’s e-book Special Projects Manager, and Dr. Andrew Sixsmith, editor for this book series, members of the Science and Technology for Aging Research (STAR) Institute, of Simon Fraser University, for their guidance and insightful ideas, and to the Institute for coordinating this book series. The authors sincerely appreciate the external reviewers’ valuable comments and suggestions which helped us improve the quality of this work. The authors also offer their gratitude to the Springer Nature publishing team for supporting us in completing this book, especially Christine Kiilerich, for her support and ongoing patience. v

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Acknowledgements

Finally, this book would not have been possible without the unfailing support of our loved ones. A heartfelt thanks for putting up with our erratic schedules and endless rewrites. AGE-WELL NCE Inc. (www.agewell-nce.ca) is Canada’s Technology and Aging Network. The pan-Canadian network brings together researchers, older adults, caregivers, partner organizations, and future leaders to accelerate the delivery of technology-based solutions that make a meaningful difference in the lives of Canadians. AGE-WELL researchers are producing technologies, services, policies, and practices that improve quality of life for older adults and caregivers and generate social and economic benefits for Canada. AGE-WELL is funded through the Government of Canada’s Networks of Centres of Excellence (NCE) program. The STAR (Science and Technology for Aging Research) Institute (www.sfu.ca/starin stitute) at Simon Fraser University (SFU) is committed to supporting community-engaged research in the rapidly growing area of technology and aging. The Institute supports the development and implementation of technologies to address many of the health challenges encountered in old age, as well as addresses the social, commercial, and policy aspects of using and accessing technologies. STAR also supports the AGE-WELL network.

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part I

1 3 5

The Supportive Smart Home

2

Aging in Place: Technology-Supported Homes . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 13

3

Evolution of the Smart Home and AgeTech . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 A Short History of Supportive Smart Homes . . . . . . . . . . . . . . . . . . . . . . 3.2 Find Out More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 15 20 21

4

Community-Dwelling Adults: Aging Well at Home . . . . . . . . . . . . . . . . . . . . . 4.1 The Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 What’s in This Chapter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Persona, Scenario, and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Deniz Kaplan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Find Out More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23 23 24 25 25 32 32 32

5

Sensor Technologies: Collecting the Data in the Home . . . . . . . . . . . . . . . . . . 5.1 The Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 What’s in This Chapter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 How to Select a Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Low-Cost Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Medium-Cost Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 High-Cost Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

35 35 38 38 38 42 44

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6

Contents

5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Find Out More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

46 46 52

Data Collection and Analysis Tools: From the Home to the Cloud . . . . . . . . 6.1 The Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 What’s in This Chapter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Data Collection and Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 How Much Data Are Too Much? . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Transporting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Data Isn’t Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Converting Data Into Information . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.5 Data Analysis: How Many Computers? . . . . . . . . . . . . . . . . . . . . 6.3.6 Data Analysis: Where to do This Work? . . . . . . . . . . . . . . . . . . . 6.3.7 Many Homes Generate More Data . . . . . . . . . . . . . . . . . . . . . . . . 6.3.8 Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.9 Multi-sensor Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.10 What to do With Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.11 Feedback to Support Independence . . . . . . . . . . . . . . . . . . . . . . . 6.3.12 System Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.13 System Cost and Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Key Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 In-Home Hub Systems and Solutions . . . . . . . . . . . . . . . . . . . . . 6.4.2 Multi-sensor Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Internet and Cellular Network Evolution . . . . . . . . . . . . . . . . . . . 6.4.4 Cloud-Based Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.5 Pattern Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.6 Supportive Smart Home End-User Notifications . . . . . . . . . . . . 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Find Out More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 53 55 55 55 56 57 57 59 59 60 61 62 62 63 63 64 66 66 66 66 66 67 68 69 69 69

Part II 7

Applying Supportive Smart Home Technology to Key Elements of Aging

Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 The Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 What’s in This Chapter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Persona, Scenario, and Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Antonio Bellotti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

73 73 75 75 75 82

Contents

ix

7.5 Key Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Find Out More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

83 83 83

8

Cognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 The Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 What’s in This Chapter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Personae, Scenarios, and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Josette Lariviere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Marie Blanchet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 Robert Fairbanks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Caring for the Care Partner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Key Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7 Find Out More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

85 85 86 86 86 90 95 97 99 100 100 100

9

Medical Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 The Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 What’s in This Chapter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Personae, Scenarios, and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Michaela Schmidt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 Karin Nilsson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.3 Zhao Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Key Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

103 103 104 104 104 106 109 111 111 111

10 Activities of Daily Living . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 The Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 What’s in This Chapter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Personae, Scenarios, and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 Alfred Ziegler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.2 Robert Brown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.3 Sunita Kumar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Key Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Find Out More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

113 113 114 114 114 117 120 123 123 124 124

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Part III

Contents

The Future of Supportive Smart Homes

11 Future of the Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 The Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 What’s in This Chapter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.1 Sensor Technology Advancements . . . . . . . . . . . . . . . . . . . . . . . . 11.2.2 Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.3 Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Find Out More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

129 129 130 131 136 137 139 139 139

12 Next Steps in Ethics and Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 The Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 What’s in This Chapter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 Persona, Scenario, Solution, and Ethical Principles . . . . . . . . . . . . . . . . . 12.4.1 Julia Simpson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.2 Ethical Issues Related to Technology . . . . . . . . . . . . . . . . . . . . . . 12.5 Public Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.6.1 Key Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.6.2 Find Out More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

141 141 142 142 142 142 148 152 155 156 156 157

13 Reflections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1 Our Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Transdisciplinary Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 One Last Case—Closer to Home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

159 159 161 166 168

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Authors

Dr. Frank Knoefel, M.D. is a Physician at the Bruyère Memory Program, Ottawa, ON, Canada, a Senior Investigator with the Bruyère Research Institute, and an Affiliate Researcher with Carleton University. He obtained his M.D. at l’Université de Montréal, Family Medicine at l’Université de Sherbrooke, and his Fellowship in Care of the Elderly at the University of Ottawa. In addition to his appointment as an Associate Professor with Ottawa University’s Faculty of Medicine, he is also an Adjunct Research Professor in Systems and Computer Engineering at Carleton University. His research expertise is in the use of sensors and data analytics to facilitate aging in place, initially focusing on mobility, and more recently working on cognition. Currently, he is working on sensor applications for cognition both in a smart supportive home and using driving simulators. He holds a Master of Public Administration and a Fellowship and Diploma in Care of the Elderly with the College of Family Physicians of Canada. Recently, he was appointed as University of Ottawa Brain and Mind—Bruyère Research Institute Chair in Primary Health Care Dementia Research. In his spare time, Frank enjoys spending time with his family: snowboarding, sailing, photography, music, and amateur construction. e-mail: fknoefel@ bruyere.org

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About the Authors

Bruce Wallace, Ph.D., P.Eng. is a Executive Director of AGE-WELL Sensors and Analytics for Monitoring Mobility and Memory National Innovation Hub (SAM3 ), Adjunct Research Professor with the Systems and Computer Engineering Department, Carleton University, Ottawa, ON, Canada and an Affiliated Researcher with the Bruyère Research Institute. He holds a Master and Ph.D. in Engineering from Carleton University and has studied business at MIT Sloan and UNC Kenan-Flagler. With over 20 years of experience in the high technology sector, his current research interests include the application of sensors, signal processing, and big data analytics—especially as applied to cognition measurement with a specific focus on aging and independence. His work includes applications within personal residences, personal vehicles, hospitals, and other care settings. He is a Senior Member of the IEEE. Outside of work, his interests include swimming, canoeing, music, and distance cycling. e-mail: [email protected] Dr. Neil Thomas, M.D. is a Cognitive and Behavioral Neurologist at the Bruyère Memory Program, an Assistant Professor of Neurology, Department of Medicine, University of Ottawa and an Affiliate Investigator, Bruyère Research Institute in Ottawa, ON, Canada. He holds a Master of Science in Neuroscience from Queen’s University. He completed his M.D. and neurology residency training at the University of Ottawa, and a Fellowship in Geriatric Neurology at the Oregon Health and Science University. His research focus is on using digital biomarkers to objectively measure and quantify changes in cognition and functional activities in individuals with cognitive impairment. He is also interested in improving the assessment of time and effort spent on caregiving tasks using sensor-based objective outcome measures. He is a member of the American Academy of Neurology, the International Society to Advance Alzheimer Research and Treatment, the AGE-WELL National Innovation Hub in Sensors and Analytics for Monitoring Mobility and Memory, and the Consortium of Canadian Centres for Clinical Cognitive Research. He enjoys spending time with his wife and children, especially at the family cottage, kayaking, and skiing. e-mail: nthomas@ bruyere.org

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Heidi Sveistrup, Ph.D. is a Full Professor in the School of Rehabilitation Sciences, Faculty of Health Sciences, at the University of Ottawa and an Adjunct Research Professor in the Department of Systems and Computer Engineering at Carleton University. She received her Master of Arts in Biomechanics from McGill University, and her Ph.D. in Neuroscience from the University of Oregon. Her research focuses on rehabilitation and the use of technologies to support wellness, engagement, and long life. As the former CEO and Chief Scientific Officer of the Bruyère Research Institute, and VP, Research and Academic Affairs at Bruyère Continuing Care, a multi-site academic health care organization, she was the Executive Director of the Ontario Centres Learning, Research and Innovation in Long-Term Care at Bruyère. She is a project lead and scientist with AGE-WELL, a senior investigator with the Bruyère Research Institute and continues to work with the CAN Health Network. Heidi has contributed to the advisory and scientific councils of the University of Ottawa’s Brain and Mind Research Institute, the Canadian Accessibility Network, and the Canadian Standards Association work on long-term care standards. When not working, Heidi enjoys reading, boating, and time with family. e-mail: [email protected] Rafik Goubran, Ph.D., P.Eng. is Vice-President (Research and International) and Chancellor’s Professor at Carleton University, Ottawa, ON, Canada. He received his Ph.D. in Electrical Engineering from Carleton University in 1987. His research expertise is on sensor applications, data analytics, and audio signal processing. His current research projects focus on patient monitoring and the design of smart homes for the independent living of seniors. He served as Dean of the Faculty of Engineering and Design and Chair of the Department of Systems and Computer Engineering at Carleton University and was the founding Director of the OttawaCarleton Institute for Biomedical Engineering. Rafik is a Life Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, a member of the Association of Professional Engineers of Ontario, and an Affiliate Investigator with the Bruyère Research Institute. e-mail: [email protected]

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About the Authors

Christine L. Laurin, M.A. holds a Master of Arts Communication from the University of Ottawa and Masters Certificate in Project Management from Carleton University and the Schulich School of Business at York University. After 16 years in Ottawa’s high-tech sector, Christine joined the Medical Affairs team at Elisabeth-Bruyère Hospital, Ottawa, ON, Canada to support physicians with the Bruyère Memory Program. As a Research Coordinator with the Bruyère Research Institute, she also supports AGE-WELL National Innovation Hub on Sensors and Analytics to Measure Mobility and Memory (SAM3 ) as their Coordinator of Knowledge Translation. She is interested in how visual texts help us to communicate and comprehend, and how these then contribute to a ‘distribution of the sensible.’ Christine, aka Teena, enjoys spending time with family and friends, hiking, gardening, painting, and laughing. e-mail: [email protected]

Abbreviations

ADL AI bADL CABHI CIHR DIKW eADL EU HUI3 iADL MCI NCE NIH NSERC NWDD OBIO OCI OHSU ORCATECH RGD-D SAM3 TIM-HF TUG WHO

Activities of Daily Living Artificial Intelligence Basic Activities of Daily Living Centre for Aging + Brain Health Innovation Canadian Institutes of Health Research Data Information Knowledge Wisdom pyramid Enhanced Activities of Daily Living European Union Health Utility Index Mark 3 Instrumental Activities of Daily Living Mild Cognitive Impairment Networks of Centres of Excellence National Innovation Hub Natural Sciences and Engineering Research Council of Canada Nocturnal Wander Detection and Diversion Ontario Bioscience Innovation Organization Ontario Centre of Innovation Oregon Health and Science University The Oregon Center for Aging and Technology Red, Green, Blue—Depth Sensors and Analytics for Monitoring Mobility and Memory (an AGEWELL NIH) Telemedical Interventional Monitoring in Heart Failure Timed-Up-And-Go World Health Organization

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Introduction

If there is one characteristic each of us shares, the world over, it is that we are aging! The World Health Organization estimates that the population of adults over the age of 60 will increase from 1 billion in 2020 to 2.1 billion in 2050 (2022). Life-expectancy trends continue to rise and people living into their 100s are not as uncommon as what it once was. With luck, living many years will result in countless positive outcomes, but it will certainly not be without its challenges either. As we age, our understanding of people and the world around us increases. We are able to put challenging situations into more manageable contexts. We deal better with social conflict and have improved ability to regulate our emotions. And in contrast to the perception that aging is associated with sadness, research shows that older adults are happier than younger adults and their emotions are less likely to jump around. In fact, sadness, anger, and fear become less pronounced as we age (Carstensen and Mikels 2005; Carstensen et al. 2003). Also, growing more conservative in financial aspects of our lives as we age protects us against the stress of losses. There are many positive aspects to aging, however, from a physiological point of view, aging can come with various bodily changes. Our cellular biology changes (e.g., decreased activity of osteoblasts in bones), our body system changes (e.g., osteoporosis in multiple bones of the spine), and our functional level changes (e.g., exaggerated curvature of the spine and subsequently impaired mobility). These changes will ultimately affect our physical and cognitive abilities. Cellular changes can result in symptoms (what the aging adult feels) and clinical signs (what clinicians find upon examination), and these may be regrouped into diseases. These diseases, in turn, may affect day to day functioning. Changes in functioning, in turn, can affect health and safety, and the ability to live independently. When experiencing difficulty with activities of daily living, our usual options are to get support from other people, such as informal caregivers (family members, neighbors)

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 F. Knoefel et al., Supportive Smart Homes, Synthesis Lectures on Technology and Health, https://doi.org/10.1007/978-3-031-37337-4_1

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1 Introduction

or formal caregivers (community support, nursing, personal support worker, therapists). However, if these resources are insufficient in aiding our daily activities we may ultimately, need to move into a communal-living facility (e.g., retirement home or long-term care facility) that can provide greater support and safety. Before we continue, it would be important to talk about ‘home.’ Academics recognize that the definition of home is hard to pin down, because it “functions as a repository for complex, inter-related and at times contradictory socio-cultural ideas about people’s relationship with one another, especially family, and with places, spaces, and things” (Mallett 2004, p. 83). Our concept of home, and what we feel constitutes home, will not necessarily represent home for another person. This is because each “home features a unique and dynamic combination of personal, social and physical properties and meanings” (Sixsmith 1986, p. 294). Needless to say, home for older adults represents much more than a structure that provides protection from the elements. A study by Sixsmith and Sixsmith (2008) found that for older old adults living in the UK, home: • Provides a sense of independence, privacy, comfort, and security; • Is an important social place; and • Can be seen as a material and symbolic medium for preserving independence. There is little doubt that the physical environment in a home can provide both positive (e.g., having an exercise room) and negative (e.g., dampness) impacts on health. While a home can provide a social space for friends and family, a home with no visitors could also become a virtual prison. The World Health Organization’s (WHO) constitution retains the principle that “health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” (2020, p. 1) and we want our homes to be able to support each of these elements so as to maintain our quality of life. Aging in place has become a popular strategy around the world. The WHO (2004) define aging in place as “meeting the desire and ability of people, through the provision of appropriate services and assistance, to remain living relatively independently in the community in his or her current home or an appropriate level of housing. Ageing in place is designed to prevent or delay more traumatic moves to a dependent facility, such as a nursing home” (p. 9). More recently “the idea has broadened to remaining in the current community and living in the residence of one’s choice” (Vanleerberghe et al. 2017, p. 2900). In some cases, the family home has the potential to be adapted to meet any current and future needs, and in others, the older adult may move into a new home that is more suitable for their needs. In addition, there are financial benefits to aging in place, as bringing supportive services into the home can be done at lower cost than building and staffing long-term care facilities (Sixsmith 2013). However, there can be barriers to aging in place, such as physical ones (e.g., stairs), environmental ones (e.g., age of the building), and social ones (e.g., isolation). This is where new information and communication technologies, designed with older adults in mind, provide capacities to

1.1

Overview of the Book

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support our desires to age in place. Our book will focus on sensor technologies, often labeled AgeTech, which can benefit older adults’ quality of life and support them to age well in the right place. AgeTech includes many types of technologies. There are many low-tech devices that facilitate aging in place, such as raised toilet seats and reachers. And there are many different types of high-tech devices that facilitate aging in place. These include wearable devices (e.g., wrist-worn exercise monitors), devices embedded into the environment (e.g., wall-mounted motion sensors), and mobile devices, which can include sensorized vehicles (e.g., semi-autonomous cars) and social robots (e.g., humanoid robots that acts as a social companion). This book will focus on embedded sensors that are part of a smart home environment. Although this book gives examples of technology aimed at older adults living in freestanding houses or individual living units, much of the technology can be adapted and used in communal living situations (Knoefel et al. 2022). While this book targets the older adult, their families, and their care team, there is increasing interest in the ubiquitous implementation of supportive smart homes that can support accessibility and universality for all (Constellation Community 2018; Minot 2021). In addition, in no way are we trying to imply that people can be wholly replaced by supportive smart home systems. There can be no replacement to the quality and variety of the health and well-being supports and relationships that other humans provide, especially when those others are loved ones (Fig. 1.1).

1.1

Overview of the Book

This book is organized into three parts: (I) The Supportive Smart Home, (II) Applying Supportive Smart Home Technology to Key Elements of Aging, and (III) The Future of Supportive Smart Homes. In Part I: The Supportive Smart Home, we present the components that constitute an aging in place home that is supportive and smart. Chapter 2 discusses how we might approach aging in place through information and communication technology models. Chapter 3 provides a brief history on the supportive smart home and AgeTech. We will have some fun in this by starting with cavemen and quickly advancing to the current state. Chapter 4 provides a demonstration on how out-of-the-box smart home technologies can support our activities of daily living. Chapters 5 and 6 introduce and focus on sensor technologies that expand a smart home by adding functionality that results in a supportive smart home. We provide an overview of smart home sensors and how information from these sensors are used to monitor health and well-being of older adults. In Part II: Applying Supportive Smart Home Technology to Key Elements of Aging, we apply some of the technology from Part I to areas of aging. Chapter 7 looks at mobility change, Chap. 8 covers changes in cognition, Chap. 9 discusses accumulating medical

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Fig. 1.1 Roswitha Knoefel (right) next to Claus Knoefel (both in their 80s), aging well in the company of their daughter Constanze Betzl (left) and her husband Bernhard Betzl. Used with permission

conditions, and Chap. 10 includes changes in activities of daily living. These chapters provide examples of older adults living with these challenges and how technology can help them age in place. Part III: The Future of Supportive Smart Homes completes this book by looking at next steps in the supportive smart home story. Chapter 11 identifies some future work on sensors and supportive smart home technologies. Through a lens of ethics and public policy, Chap. 12 provides a thoughtful introduction to the impacts of supportive smart technologies on individuals and societies. We conclude by what we hope various readers will have taken away from the book and provide a personal story underlining ongoing challenges with current solutions. At the end of most chapters, you will find Key Initiatives and Find Out More sections. Key Initiatives highlight current undertakings within that chapter’s area. Find Out More provides links to items mentioned in the chapter that you may want to peruse further. To help illuminate many of the discussions in this book, we will use personas and stories to aid understanding. It is important to note: the stories, names, characters, and incidents portrayed in this book are fictitious and do not represent any actual persons (living or deceased). In addition, this technology field is growing and changing rapidly and hence, the sensor technologies and applications are considered current at time of publishing only.

References

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Our book, focusing on Supportive Smart Homes, is part of a series that explores AGE-WELL’s eight challenge areas: (1) Supportive Homes & Communities, (2) Health Care & Health Service Delivery, (3) Autonomy & Independence, (4) Cognitive Health & Dementia, (5) Mobility & Transportation, (6) Healthy Lifestyles & Wellness, (7) Staying Connected, and (8) Financial Wellness & Employment (AGE-WELL 2020). These challenge areas were identified by AGE-WELL, as a result of a comprehensive review of policy priorities relating to older adults, from governments across Canada and internationally, and subsequent feedback from over 1000 stakeholders (older adults, caregivers, service providers, and industry) (AGE-WELL News 2018). The first book in the series is by Sixsmith et al., AgeTech, Cognitive Health, and Dementia, and the second book is by Liu et al., Autonomy and Independence: Aging in an Era of Technology. This book is meant to be introductory, directed at older adults and their care partners, students and professionals in engineering and computer science, healthcare, and public policy, as well as companies in the AgeTech space. The book is written in a way that provides a review in your area of expertise but hopefully challenges you to reflect on the other points of view. Happy Reading!

References AGE-WELL (2020) AGE-WELL Challenge areas. https://agewell-nce.ca/challenge-areas. Accessed 31 Mar 2023 AGE-WELL News (2018) AGE-WELL launches Canada’s technology and aging research agenda at science policy conference. https://agewell-nce.ca/archives/7328. Accessed 17 Mar 2023 Carstensen LL, Mikels JA (2005) At the intersection of emotion and cognition: aging and the positivity effect. Curr Dir Psychol Sci 14:117–121. https://doi.org/10.1111/j.0963-7214.2005.003 48.x Carstensen LL, Fung HH, Charles ST (2003) Socioemotional selectivity theory and the regulation of emotion in the second half of life. Motiv Emot 27:103–123. https://doi.org/10.1023/A:102456 9803230 Constellation Community (2018) Best ways to make your home a kid-friendly smart home. https:// blog.constellation.com/2018/07/10/smart-home-technology-for-kids/. Accessed 17 Mar 2023 Knoefel F et al (2022) Implementation of smart supportive dementia technology in a hospital transitional care setting using human-centred design. Healthc Manag Forum 35(5):318–323. https:// doi.org/10.1177/08404704221103537 Mallett S (2004) Understanding home: a critical review of the literature. Sociol Rev (Keele) 52(1): 62–89. https://doi.org/10.1111/j.1467-954X.2004.00442.x Minot D (2021) Smart home technology and autism. Autism Spectrum News. https://autismspectr umnews.org/smart-home-technology-and-autism/. Accessed 17 Mar 2023 Sixsmith J (1986) The meaning of home: an exploratory study of environmental experience. J Environ Psychol 6(4):281–298. https://doi.org/10.1016/S0272-4944(86)80002-0 Sixsmith A (2013) Technology and the challenge of aging in technologies for active aging. Springer, US (International Perspectives on Aging), pp 7–25. https://doi.org/10.1007/978-1-4419-8348-0_ 2

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Sixsmith A, Sixsmith J (2008) Ageing in place in the United Kingdom. Ageing Int 32:219–235. https://doi.org/10.1007/s12126-008-9019-y Vanleerberghe P et al (2017) The quality of life of older people aging in place: a literature review. Qual Life Res 26(11):2899–2907. https://doi.org/10.1007/s11136-017-1651-0 WHO (2004) A glossary of terms for community health care and services for older persons. WHO Centre For Health Development: ageing and health technical report, vol 5, p 9. https://apps.who.int/iris/bitstream/handle/10665/68896/WHO_WKC_Tech.Ser._04.2.pdf?seq uence=1&isAllowed=y. Accessed 1 Apr 2023 WHO (2020) Basic documents 49th edition. World Health Organization Governance. https://apps. who.int/gb/bd/. Accessed 27 Mar 2023 WHO (2022) Ageing and health. World Health Organization Newsroom. https://www.who.int/newsroom/fact-sheets/detail/ageing-and-health. Accessed 27 Mar 2023

Part I The Supportive Smart Home

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Aging in Place: Technology-Supported Homes

Population aging is transforming our society in myriad and interesting ways and so is information and communication technology (ICT). ICTs consist of a “diverse set of technological tools and resources used to transmit, store, create, share or exchange information” (UIS 2020). More unambiguously, they are devices (e.g., smartphones, computers, tablets), and applications (e.g., email, the web, instant messaging, social media) that provide us access to information and enable electronic forms of communication (e.g., have video chats or send text messages). More recent ICTs advancements, beginning in the early 2010s, introduced opportunities in ICTs’ abilities to have positive impacts on aging populations’ health and vitality. One such area is to enhance our ability to age in place, and just like our individual experiences with aging, the ICT tools to support aging in place are varied. When we think of technology for homes, a house alarm system may come to mind, wherein, the home alarm system’s essential job is to detect motion in the home when there shouldn’t be any. For instance, when on vacation, one would not expect to have motion in the home. Similarly, one would not expect a window or the back door to open when we are away. Other home technology examples could include those for entertainment use and automated lighting. We can use our smartphone to create a favorite songs playlist and then while preparing breakfast, we could ask the smart home to play the music from that playlist ‘handsfree.’ Similarly, we may want the smart home to turn on the kitchen lights when our hands are full of grocery bags. If the kitchen lights are connected to our smart home, we can ask the home to turn them on, maybe even dim them, or even change the light’s color or brightness. And, in some smart home systems, we can even get our lights to pulse to the music streaming from our favorite playlist. Each of these systems has a programmed role, and these programs present the possibility of making our lives easier. But a supportive smart home would need to be able to do these and so much more. Supportive smart home technologies need to go beyond providing these types of services

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 F. Knoefel et al., Supportive Smart Homes, Synthesis Lectures on Technology and Health, https://doi.org/10.1007/978-3-031-37337-4_2

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and should be able to monitor the health and well-being of the inhabitant; and ultimately, provide an action if things are out of the ordinary. Subjective feelings about our health/well-being are important, but science prefers that we measure health. Science then establishes averages and normal ranges that we can compare ourselves against, such as healthy weight for a given sex and height. Science also provides recommendations on the number of steps to take per day, the ideal foods to eat, and the safe number of alcoholic drinks to consume. In much the same way, there are methods of measuring the impacts of normal and pathological aging: • Direct health measures (e.g., body weight); • Complex health measures (e.g., memory, orientation); and • Measures of function that typically combine many health domains (e.g., walking, taking medications, using a computer, dressing). Typically, these are inquired upon by healthcare professionals, and some can be measured in a health clinic. But these measurements, if they are precise, only partially reflect the reality of the life of the person. For instance, if our weight in the doctor’s office is the same as it was 1 year ago, this measurement does not guarantee that our weight did not fluctuate during the intervening 364 days. In fact, in some circumstances, for instance sleep patterns, there is gradual increase in fluctuations of patterns before there is a more significant change. Therefore, more frequent, possibly even continuous measurement would provide a better sense of the health condition of the person over time. However, how do we ensure that we get weight readings twice a week for a year. First, we must remember to do this, and then we have to want to get on the scale that often. Maybe this is where technology can help? On the first issue, we can program a system to remind us every Wednesday and Saturday to weigh ourselves. What if we could solve both issues by incorporating a scale into our bed? Now we don’t have to remember nor want to get on the scale. The promises of technology are many—but they are not without their challenges. It is important to clarify the differences between medical conditions and wellness. Medical conditions include diseases, for example diabetes, hypertension, heart failure, and cancer. On the other hand, wellness can include physical, psychological, and social wellbeing. This is particularly important when we distinguish between devices that monitor medical conditions and those that monitor wellness. Medical devices require extensive testing to ensure they meet medical standards, for example blood pressure monitoring device standards (Stergiou et al. 2018). On the other hand, wellness devices typically do not have any standards, for example, devices that measure daily step counts (Treacy et al. 2017). Ideally, this supportive smart home would be able to monitor simple health values (e.g., body weight), complex health issues (e.g., memory), and complex wellbeing activities (e.g., taking medication). This latter activity is complex because it has (1) a cognitive

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Fig. 2.1 Copyright © 2023 SAM3 Supportive Smart Home Model

component: remembering when to take the pill; (2) a fine motor component: opening the container; and (3) a motivation component: wanting to take the medication. The supportive smart home would not only monitor these activities but also produce an action, should one be required. In the case of older adults taking their medication (compliance), the supportive home’s action could start with the inhabitant of the home first: “Please take your morning pills.” There may be times when the system needs to escalate to an informal caregiver: “Your Dad has not taken his morning pills.” Finally, some of the knowledge may also be useful to formal caregivers: “Mr. Smith has missed taking his morning pills three times this week.” A supportive smart home takes data from a monitoring sensor system, processes it in real time, and creates an action (Fig. 2.1). This action may be a local infrastructure action, such as turning on a light. It may be a verbal or visual reminder or cue to the older adult. Or the action could be a notification to an informal or formal caregiver or a healthcare professional. As mentioned in the Introduction, in this book, we focus on sensors that are either attached to the home (e.g., motion sensors attached to a wall) and sensors or devices that may not be attached but mostly stay in the home (e.g., weight scale). While wearable sensors and social robots can collect important well-being information, and they could (or should) be connected to a supportive smart home system, they will not be addressed directly in this book.

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Throughout this book, we will refer to three categories of individuals who provide support to older adults desiring to age in place. While different terms are used in various domains, we will refer to: Healthcare professionals: physicians, pharmacists, occupational therapists, physiotherapists, registered nurses, licensed or registered practical nurses (LPNs/RPNs), and any other members of a health profession with a professional college. Formal caregivers: personal support workers (PSWs), and others paid to provide care to people in their homes. Informal caregivers: family members, neighbors, friends, volunteers, and anyone else not paid to provide care. We also define terms that will be used when we discuss sensor technology in supportive smart homes. Specifically, we will distinguish between data, information, knowledge, and wisdom. In this book, we will use the following understandings: Data are numbers/measurements that come from a sensor, which typically have little meaning by themselves. Information is created when the sensor data are organized in a way that has meaning to a particular individual. Knowledge is sensor information that has been processed, analyzed, and interpreted, and can then be used to perform an action. Wisdom is the synthesis of the sensor’s information and knowledge, along with the experience in understanding the patterns that occur, so as to apply a higher degree of knowledge and action. To demonstrate these terms, let us take the example of data point ‘79.’ This has little meaning by itself. However, if this data is coming from a scale in a particular home, on a particular date, it becomes information: Mr. Liang, 79 kg at 09:15 on July 1, 2022. To convert this to knowledge we might compare to yesterday’s information on Mr. Liang’s weight—now the knowledge is that this is an increase of 2 kg from yesterday. Knowledge will typically lead to an action. In this case, Mr. Liang’s weight change could result in the system sending a notification to his informal caregiver and his physician. This becomes wisdom when the physician, aware that Mr. Liang lives with heart failure, recognizes he would benefit from an adjustment to his medication. Ultimately, this book will explore how recent changes in smart home technologies have created the ability to extend the time aging adults can remain at home, allowing them to age in place. With more and smarter use of technologies, we will continue to extend this time, more safely and with greater accessibility.

References

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References Stergiou GS et al (2018) A universal standard for the validation of blood pressure measuring devices: Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) Collaboration Statement. J Hypertens 36(3):472–478. https://doi.org/10.1097/HJH.0000000000001634 Treacy D et al (2017) Validity of different activity monitors to count steps in an inpatient rehabilitation setting. Phys Ther 97(5):581–588. https://doi.org/10.1093/ptj/pzx010 UIS (2020) Information and communication technologies (ICT). UNESCO Institute of Statistics. https://uis.unesco.org/en/glossary-term/information-and-communication-technologies-ict. Accessed 27 Mar 2023

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Evolution of the Smart Home and AgeTech

There are a number of ways to review the history of anything. There are short histories and long histories. Some are focused on a single event, likely including some lead up, and others focus on themes that evolve over centuries. In this chapter, we are going to have some fun. We will look at the history of homes and how activities in the home have been monitored, beginning with caves and continuing through simple measurement of home utility use, and finally ending with Internet-connected homes.

3.1

A Short History of Supportive Smart Homes

In the Beginning, Homes Were for Shelter. From the time of cave dwelling to the time of building pre-grid cabins using local material—homes were not very smart. Their role was to protect you from the elements and from predators. If you were cold in the dwelling, you could light a fire. If you were hot, you might go to the nearest body of water to cool. If it was dark and cold, you could again light a fire, or if only light was needed you could light a torch or eventually a candle. Over several thousands of years, construction technologies advanced, but each building was independent from the next, and required people to manage heat and light on their own. It could be argued that the presence of smoke represented the first time that activity in the home could be monitored—what we call today “presence technology.” Homes Learned to Count. It is with the invention of shared utilities that this all changed. As soon as people wanted electricity in their homes—the producers of electricity had to find a way of billing them—hence the invention of meters that could measure electricity flow. And while water had been redirected for humans’ use for some 6000 years, it was only in the last 50 years or so that water use was measured at the individual home level. The same happened for homes that were connected to natural gas.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 F. Knoefel et al., Supportive Smart Homes, Synthesis Lectures on Technology and Health, https://doi.org/10.1007/978-3-031-37337-4_3

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Technology in Homes. Some authors refer to two overall types of technologies for the home: time-saving and time-using. Time-saving devices were supposed to make housekeeping tasks easier (e.g., vacuum cleaners, washing machines), whereas, time-using devices created new ways to spend time in the home (e.g, listening to the radio or record player, watching television). Next, telephones were added to homes, allowing for another device whose usage could be not only measured for invoicing purposes but also created the first electronic identification of a ‘home.’ Interestingly, the benefits of this novel communication technology were so clear that people did not worry about the fact the telephone company knew who you were talking to. It was only after years of surveillance that individual rights to privacy on phone lines were established. Homes with Technology. The next piece of technology that made homes smarter was the thermostat. The thermostat is a sensor for temperature that was designed to start the furnace if the temperature dropped below a certain temperature and then turn it off when the desired temperature was reached. With the addition of air conditioners into homes, the inverse could be done as well: if the temperature rises above a certain temperature,

Fig. 3.1 Components in the history of smart home components

3.1

A Short History of Supportive Smart Homes

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the air conditioner turns on until the temperature drops to the desired temperature. Now, the home is smart enough to keep the temperature at the occupant’s preferred range. The next domain to make our homes ‘smarter’ was to make them more secure. One could now add some sensing, for instance, the addition of a magnetic switch to a door could tell if it was open or closed, and an infrared motion sensor could detect movement. Now, alarm companies could be informed when the occupant was leaving for an extended period. Presumably, while the occupant is away, there should be no door openings or movement within the home. So, if there was opening of doors or movement within the home, the alarm company could receive a notification, via signal through the telephone line, for instance. A unique identifier could then be used to identify the address that needed to be checked. Homes with Computers. Finally, with the development of personal computers, especially those connected to the outside world, (i.e., to the Internet), more intelligence was brought into the home. Initially, this information was “read-only,” meaning that you could have the entire Encyclopedia Britannica available on your computer, but that information could only be used by the people reading the screen. Subsequently, connecting a home computer to computers at the bank or at the shopping center allowed people to do their banking and shopping via the computer. ‘Smart’ Home Technology. With computers now connecting to each other, it has allowed the creation of smart home technology. In the simplest of terms, homes went from having their own supply of records, CDs, and DVDs, to being able to stream content from an entertainment provider. Music and movies are now stored elsewhere, and the ‘information’ comes to your home on demand. Similarly, the addition of the mobile phone to the home allowed information streaming from the phone to the home and vice versa. Furthermore, the phone could control devices connected to a computer in the home. This allowed homes to transition from having a light switch in every room that could manually turn on/off each light, to having a ‘smart’ light switch that could be turned on using commands sent by the computer or a smartphone. The next big change was the way people were able to communicate to computers. Initially, connecting with the computer was done via a keyboard and a screen. But not everyone likes to type on a computer or phone. This requirement has changed in the last few years with the development of natural language processing. With the availability of computers having greater processing ability, and the development of artificial intelligence (AI), it made it possible for a computer to ‘understand’ what a person wanted—this allowed for the invention of virtual assistants that understand voice commands. Amazon’s Alexa and Google Home are two of the top smart home technology providers. They rely on a device placed in each room that you wish to convert into a ‘smart room.’ Typically, these devices have a microphone that can understand simple instructions, e.g., “Alexa turn on kitchen lights,” or “Google play my play list: Rock’n’roll.” Typically, these systems need to be connected to a personal computer (or smartphone), so that rules and lists can be created. Over time, more and more electronic devices can be connected

18

3 Evolution of the Smart Home and AgeTech

to the ‘smart home hub’ so that the home’s temperature can be adjusted, shopping lists can be created, and the virtual assistant can be connected to information on the Internet. As radio stations are now accessible through the Internet, it is possible to ask the smart home to “Play CBC Radio 1 Ottawa.” Similarly, the virtual assistant can be asked to find information, such as “What is the capital of Mauritius?” Recently, Amazon partnered with Connected Canadians, to launch Alexa Smart Properties in retirement communities across Canada. The aim is to incorporate Alexa technology to help residents be better connected, informed, and entertained, while also potentially facilitating care team efficiency (Bruyère News 2022). A visual summary of the evolution of smart home technology is provided in Fig. 3.1. ‘Smart Monitoring’ Homes. At the same time smart home technology was developing to make life easier for the occupant (e.g., virtual assistant), several groups were looking at the benefits of home monitoring technology to determine the well-being of the occupants. In the context of frail, older adults living by themselves, a smart monitoring home could help identify changes in well-being. Theoretically, given the right sensor(s), any behavior or activity in a home could be monitored for safety and signs of illness or functional decline—and this would be an example of AgeTech applied to homes. One of the most widely distributed smart monitoring home system is the researchfocused platform developed at the Oregon Health and Sciences University (OHSU) called Collaborative Aging Research using Technology (CART), see Fig. 3.2. The project was founded by Dr. Jeffrey Kaye, a neurologist specializing in cognitive decline, with the intent of being able to identify ‘digital biomarkers’ associated with cognitive decline. The sensors cover all areas of home and daily living. Included are door sensors, motion sensors, computer activity sensors, daily activity/sleep wearable sensors, pressure-sensitive bed mats, a medication monitoring sensor and a sensor that goes into the car. These sensors are connected to a hub that sends the data to computer banks at OHSU. To date over 400 of these sets have been deployed in people’s homes across the United States, Canada, and emerging sites in France and Asia. This setup allows CART researchers around the world to access these data to better understand how the sensor data from older adults’ homes can be converted to knowledge. The Near Future: Supportive Smart Homes. As our understanding of digital biomarkers improves, and the smart activities of smart homes become more flexible, the technology will gradually transition to supportive smart homes. These homes will not only monitor the activities of their occupants but will also be able to analyze the data in real-time and provide immediate support. A number of groups are working on this technology, including Carelink Advantage, and esprit-ai. One classification system of smart homes that shows promise for the future is by Sovacool and Furszyfer Del Rio (2020) (Fig. 3.3). Let’s demonstrate a supportive smart home potential, by using an example of monitoring the making of dinner by the occupant. What we call the supportive smart home would

3.1

A Short History of Supportive Smart Homes

Fig. 3.2 Copyright © CART Home Model of Smart Home Monitoring Level 0 1

Name Basic Isolated

2

Bundled

3

Automated

4

Intuitive

5

Sentient

Description Analogue home without smart technology. Home with isolated technology, e.g., baby monitor, TV. Smart technology bundled and programmable, e.g., heat and appliances. Smart technology programmable, more automated, and anticipatory. Systems integrate to learn, modify, and adapt provision of services. Systems fully integrate and automate to meet all predicted needs.

Fig. 3.3 Smart home classification system

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3 Evolution of the Smart Home and AgeTech

correspond to levels 4 and 5 in the Sovacool classification system. Let’s say that the occupant wishes to cook potatoes: washes them, peels them, cuts them into pieces, puts them into a pot, adds water, and cooks them for 20 min. Let’s also assume that a combination of sensors and smart analyzing technology exists that can identify each of these steps. The supportive monitoring home would be able to identify if the pattern changes over time, for instance if increasingly over time the occupant forgets to add water to the pot. The smart home would keep this information and allow people to access it as needed and as appropriate, well after the fact. However, the supportive smart home would immediately identify the change in behavior and cue the occupant, for instance using a smart speaker: “Mr. Smith, would you like to add water to the pot with the potatoes?” If the water step is not corrected, in the future the system might turn off the stove and notify a family member that there is a risk of fire in Mr. Smith’s kitchen. Similarly, the supportive smart home could remind someone that they have not yet taken their medication or that today is one of the days of the week they usually wash their hair. Ultimately, older adults will decide, possibly with the help of their formal and informal caregivers and healthcare professionals, if supportive smart home technologies are going to be an advantage for them. This book is intended to help them sort through the components of supportive smart homes and demonstrate examples of the types of services they can provide. In addition, there is an entire chapter that considers ethics and policies that should be considered in the smart home space.

3.2

Find Out More

Smart Home Point is a blog site “launched in November 2019 with a view to be a central point for tips, tricks and information relating to smart home tech and home automation”: https://www.smarthomepoint.com/history/. Collaborative Aging Research using Technology (CART). https://www.ohsu.edu/collab orative-aging-research-using-technology/cart-home. Partnering with Connected Canadians, Amazon has launched the Alexa Smart Properties in Canada, bringing Alexa to senior living communities: https://press.aboutamazon. com/canada-press-center/2022/11/amazon-launches-alexa-smart-properties-in-canada-bri ngs-alexa-to-senior-living-communities or https://www.bruyere.org/en/Blog/amazon-lau nche-alexa-smart-properties-in-canada?ly=4. Carelink Advantage: is a technology provider based in Sudbury, Canada. They provide a home hub, ambient and wearable sensors and cameras to monitor such activities as medication taking to support aging in place. https://carelinkadvantage.ca/. Esprit-ai is an Ottawa, Canada Company that uses ambient sensors and artificial intelligence to monitor older adults living with cognitive decline living at home or retirement homes. https://esprit-ai.com/.

References

21

References Bruyère News (2022) Amazon launches alexa smart properties in Canada, brings alexa to senior living communities. Bruyère Continuing Care. https://www.bruyere.org/en/Blog/amazon-launchealexa-smart-properties-in-canada?ly=4. Accessed 12 Apr 2023 Sovacool BK, Furszyfer Del Rio DD (2020) Smart home technologies in Europe: a critical review of concepts, benefits, risks and policies. Renew Sustain Energy Rev 120:109663. https://doi.org/ 10.1016/j.rser.2019.109663 The CART Home|CART|ORCATECH|OHSU (no date) AHSU collaborative aging research using https://www.ohsu.edu/collaborative-aging-research-using-technology/cart-home. technology. Accessed 1 Apr 2023 The definitive history of smart home devices—smart home point (2019). https://www.smarthome point.com/history/. Accessed 1 Apr 2023

4

Community-Dwelling Adults: Aging Well at Home

4.1

The Challenge

If we have the good fortune to live into our golden years, we have surely succeeded at life. While aging is universal, just ‘how’ we age will be uniquely our own. While capacity declines can be experienced across our entire lifespan, modifiable risk factors, such as physical inactivity, can have serious implications on our health. The World Health Organization (2022) notes insufficient physical activity as attributable to 830,000 deaths annually. Studies support this link between modifiable behaviors (e.g., unhealthy diets, physical inactivity) to chronic diseases (e.g., cancer, cardiovascular disease, diabetes) and death (Ng et al. 2020; Booth et al. 2012). We need strong muscles to support our mobility and exercise remains the best approach to slow age-related declines (Roubenoff 2007). In addition, a crucial and modifiable risk factor also impacting our health is loneliness (Morr et al. 2022). Loneliness and social isolation in older adults are identified as an unaddressed health problem, where the negative effects of loneliness affect quality-of-life, cognition, subjective health, stress and depression, disability, cardiovascular disease, increased use of health care services, increased mortality, and institutionalization (Berg-Weger and Morley 2020). Therefore, we do well to protect ourselves and build practices that support our overall wellness. When we think about wellness, we often reflect on it in terms of habits that support our physical health (e.g., nutrition, exercise). However, wellness encompasses our “integration of physical, mental, and spiritual well-being, fueling the body, engaging the mind, and nurturing the spirit” (Stoewen 2017, p. 861), enabling us to live to our highest potential. This holistic integration includes our daily healthy habits which support better mental and physical outcomes. As we age, information and communication technologies (ICTs) can support activities of daily living to nurture our overall wellness. As you will read in more detail in Chap. 10, there are many activities of daily living (ADLs) we necessarily need to

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 F. Knoefel et al., Supportive Smart Homes, Synthesis Lectures on Technology and Health, https://doi.org/10.1007/978-3-031-37337-4_4

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4 Community-Dwelling Adults: Aging Well at Home

Examples of Enhanced Activities of Daily Living Use of Technology • Hand-held electronic devices (smartphones, tablets) • Social media platforms • Online shopping

Physical Activities • • • •

Gardening Curling Golfing Mobility and strength classes

Leisure Activities • Watching television • Preforming hobbies • Travelling

Social Activities • Social groups • Social events • Religious or political groups

Cultural Activities • Museums • Movies • Sporting events • Music Concerts • Symphony • Theater

Intellectual Pursuits • Current events & news • Learning new skill • Classes

Adapted from Rogers et al. 2020

Fig. 4.1 Enhanced activities of daily living

do, such as self-care tasks labeled basic ADLs (bADLs) (e.g., eating, bathing, dressing) and more complex tasks requiring planning and thinking, labelled instrumental ADLs (iADLs) (e.g., managing our finances, cleaning our home, cooking our own meals, managing our medications). Finally, enhanced ADLs (eADLs) are higher order activities that lead to “fulfillment, well-being, quality of life, happiness, or social engagement” (Rogers et al. 2020, p. 128). EADLs include many activities and are unique to the individual’s personality and autonomy, but broadly, speaking they include social relationships along with cognitive and physical leisure activities (Rogers et al. 2020). See Fig. 4.1 for some examples of eADLs. For a detailed discussion on autonomy and independence, please see the second book in this series, Autonomy and Independence: Aging in an Era of Technology, by Liu et al. As the World Health Organization’s principles hold: “health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” (2020, p. 1), wherein eADLs remain important activities that support and maintain our overall health and well-being (Fig. 4.2).

4.2

What’s in This Chapter?

When we think about aging, the assistive devices that quickly come to mind are likely canes, walkers, and wheelchairs. What we probably don’t think about are ICTs. By way of a case study, this chapter will introduce you to ICTs for community-dwelling older adults. Specifically, we will look at how virtual assistant technologies, integrated into our homes, are able to provide aging in place supports for eADLs.

4.3

Persona, Scenario, and Solutions

25

Fig. 4.2 Social connectedness. Bill and Sheila Thomas doing their favorite social activity—spending time with their grandchildren. Used with permission

4.3

Persona, Scenario, and Solutions

4.3.1

Deniz Kaplan

Persona. Deniz lives in Ottawa, ON, Canada, a high-income country with universal healthcare services. He is a first-generation Canadian, immigrating from Turkey over 50 years ago. He has been enjoying retirement after a successful career working within the Canadian Transportation Agency. He remains mentally sharp and is in relatively great shape for an 81-year-old. He suffers from some hearing loss and moderate arthritis in his hands and knees, but otherwise manages quite well. Up until a year ago, he spent his winters in Florida at a senior’s lifestyle village where he could enjoy the outdoors, fishing and golfing, along with the community’s ever-constant social activities. He is a die-hard Ottawa 67s hockey fan and likes to follow other sports that are in season.

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4 Community-Dwelling Adults: Aging Well at Home

Scenario. Deniz and his wife Isra celebrated 53 years of marriage last year, but sadly, Isra suddenly passed away 6 months ago, and he has been missing her greatly. Isra was the fabric of their social life; she organized all their family gatherings, planned their annual get-away to Florida, and even set up weekly social events (e.g., neighborhood café meet-ups) with friends and neighbors. While many have called and popped over to keep him company, he noticed these had been trailing off in the last few months. He realizes that he needs to find ways of staying connected on his own now. Isra was always the one comfortable with technology. She was their ‘emailer,’ and she would regularly get on their iPad and video chat with friends and family across Canada, Turkey, and the USA. He missed seeing the gang from Florida, so he decided to take a workshop at his local library on How to Navigate the iPad. This would help him get back to connecting with people on FaceTime. Yet, what he’s noticed even more, was how ‘old and stiff’ he feels. While initially blaming it on the grief he was experiencing, he recently acknowledged that it was likely because he had become so unmotivated and sedentary without his dearest Isra. Without her, he no longer took their usual morning walks, he had stopped doing little chores to keep the place “looking fresh.” He didn’t feel like attending social activities (e.g., bridge games at the community center) and his diet was less robust as Isra had been their family’s master chef. She was of the “use it or lose it” mindset and would press to keep them going strong with healthy meals, daily physical and mental activities, and diverse social relationships. Deniz wanted to honor his beloved Isra and get back to feeling well again. Solutions. It was during a recent conversation with his eldest daughter, Dilara, that Deniz’s interest piqued around using technology for his health and well-being. He wondered, “Might that keep his mind active and his body moving? Might it help him to remain

4.3

Persona, Scenario, and Solutions

27

connected with family and friends and social activities? Might it provide him with some daily motivation he felt was lacking in his life?” Physical Activities. As we age, physical activity has protective effects because it can decrease our risk of cognitive impairment and increase our life expectancy (Krell-Roesch et al. 2021; Yoneda et al. 2021; Halloway et al., 2020; Buchman et al. 2012). Essentially, physical activity not only increases our physical strength but it also promotes mental and cognitive health, all of which can help us to live a self-determined life at home, and for longer. Physical activity can be described as any activity that involves moving your body (e.g., cleaning your house, tending your garden, yoga, swimming, golf). As the saying goes, “it’s never too late to exercise,” because regardless of when we start, the benefits of physical activity are numerous. For Deniz, who lives with chronic arthritis, it can be difficult and painful for him to complete daily tasks, yet for arthritis, motion is considered a lotion, and it is in his best interest to return to his baseline physical activity, or more. Using persuasive technology principles in their research, Paay et al. (2022) found virtual assistants had clear potential “to persuade people to increase their physical activity at home, using Suggestion to encourage physical effort, Virtual Reward to encourage endurance, and Praise to create reassurance for beginners” (p. 416). Another novel study aimed to uncover whether virtual AI health coach led physical activity and diet intervention for community-dwelling adults was effective at improving physical activity, diet adherence, and health risk factors. Results confirmed the AI enabled “virtual health coach diet and physical activity intervention is feasible for older adults and leads to sizable health behavior change and improvements in body composition” (Maher et al. 2020, p. 9), and it has the capability of providing personalized support at scale. Box 4.1 Early personal assistant technology

Early personal digital assistants (PDAs) were handheld devices that managed your personal information (i.e., schedules, calendars, contact information). These were the precursor to our smartphones. While the term PDA is still in use, it is largely recycled to now refer to technologies that use AI and respond to voice commands. The 2010s ushered in these newest forms of digital assistants and these newer platforms, which use smart speakers to integrate with their assistants, often favor the term virtual assistant. For simplicity, we will use the term virtual assistant. A virtual assistant is “a computer program that is able to give information, play music, and perform certain tasks in response to written or spoken commands” (Collins English Dictionary, 2023). Virtual assistants work via texting (chat, email, SMS text) or voice commands (Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, Google’s Assistant), and by way of natural language processing, virtual assistants translate text or voice into executable commands. Voice-controlled assistants are incorporated in various pieces of technology, like Amazon’s Alexa, which works on smart devices like smart: lightbulbs,

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4 Community-Dwelling Adults: Aging Well at Home

thermostats, speakers, clocks, etc. Most out of the box virtual assistants come with many pre-installed capabilities, while others can be added on (Fig. 4.3). A virtual assistant can also help link you to other smart devices or software, such as accessing content available on the internet and bringing it into your home (e.g., access and cast a YouTube video to your smart TV). These voice-controlled virtual assistants can turn on your TV, give you appointment reminders, read you an eBook, and even tell you a daily joke. They never get impatient with you, even if you ask the same questions again, and again. Deniz need not worry about braving the elements of a Canadian winter during his daily walks. He can get a great workout and stay on track through the help of a virtual assistant. There are apps and virtual training platforms that he can follow at home. Deniz’s virtual assistant can even integrate with other platforms, such as wearable fitness devices (e.g., Garmin, Fitbit), or if money is no object, he can go so far as to create a home gym and link interactive gym equipment. While his wearable device can monitor heart rate, exercise time, and calories burned, he can also connect with likeminded exercise challengers. In so doing, he opens the opportunity to also gain new social connections. He could also connect with a virtual health coach (a certified online health and wellness coach) to provide him with custom programs and coaching, all within the privacy and comfort of his own home. For example, Vintage Fitness and AIM Fitness specialize in providing personalized nutrition and physical training to adults over 50, and The Wellness

Play music

Report the news

Check the weather

Offer sports updates

Play podcasts or audiobooks

Stream live radio

Control your smart home

Manage your lists and calendars

Call phones & other Alexaenabled devices

Set timers and alarms

Find local business information

Answer questions

Play audiobooks

Do math

Administer games

Tell jokes

Summon a ride

Order dinner

Find recipes

Shop on Amazon

Adapted from: https://theassistant.io/guide/what-is-alexa/

Fig. 4.3 Popular Alexa Capabilities. Users can use their voice to perform everyday tasks

4.3

Persona, Scenario, and Solutions

29

Institute provides virtual training and hosts free online wellness webinars. This notion of connecting with other fitness challengers or a virtual health coach opens us to discussions on eADLs related to the protective effects of social connectedness. Social Activities. Social connectedness is defined as “the opposite of loneliness, a subjective evaluation of the extent to which one has meaningful, close, and constructive relationships with others (i.e., individuals, groups, and society)” (O’Rourke and Sidani 2017, p. 43). It holds within us the feelings of being cared about and caring about others, and the sense of belongingness to a group or community. There are numerous life circumstances that can affect our social connectedness. These can include changes in our health and mobility, loss of family members (especially one’s spouse), loss of friends, changes in our living arrangements, even the loss of our driver’s license. Under certain circumstances, social activities can be significantly hindered even further, such as during pandemic isolation protocols, which creates not only a lack of social activities but also a severed ability to connect in person. A lack of social connectedness poses even greater risk of loneliness or depression to those living alone (Victor et al. 2000). Social relations are a “complex and dynamic set of characteristics that have been shown to distinctly affect health and quality of life across the lifespan and especially in older adulthood” (Sharifian et al. 2022, p. 17). To increase our social connectedness in meaningful ways, we can participate in activities that bolster our sense of belongingness and care, such as those in Fig. 4.4. Virtual assistants can help with our socialization by facilitating digital forms of interpersonal communication via video messaging (e.g., FaceTime, Skype, WhatsApp), text messaging (e.g., Messenger, WhatsApp, iMessage), social media (e.g., Instagram, Facebook, Twitter) and dating (e.g., Tinder, Match.com). Through voice commands to his virtual assistant, Deniz can connect with his friends in Turkey and Florida, and even with his children and grandchildren who live nearby. Video chats between young grandchildren and their grandparents can help with their bonding (Kakulla et al. 2021) (Fig. 4.5). There are also application platforms designed to prevent social isolation. Social app Amintro, developed by Charlene Nadalin, is tailored to older adults. It supports social connectedness exclusively for adults 50 years and older. It is a subscription free app that

Steps which help maintain connectedness

VOLUNTEER

JOIN COMMUNITY ACTIVITIES

USE TECHNOLOGY

Fig. 4.4 Proactive steps to help maintain connectedness

TAKE A CLASS

BE ACTIVE

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4 Community-Dwelling Adults: Aging Well at Home

Fig. 4.5 Learning technology. Robert Laurin demonstrating video chatting to his mother Agnes Laurin. Used with permission

offers non-romantic connections along with a wealth of resources that support healthy aging. Rooted in the notion of old-fashioned friendships, it connects you with others who share your interests, ambitions, and pastimes. Deniz can also use the virtual assistant as a tool to pass the time and challenge his cognition. He can ask his virtual assistant to find everything from games based on TV game shows (e.g., Jeopardy, Price is Right, Deal or No Deal), word games (e.g., Word of the Day, Heads Up), games to play with his grandkids (e.g. Sesame Street, 1–2–3 Math, Hide and Seek), games to play with friends (e.g., Song Quiz, True or False), or even well-known games (e.g., Chess, Tic Tac Toe, Twenty Questions, Bingo). Physical Health and Intellectual Pursuits. Since his wife’s passing, Deniz has put on a few pounds, likely because of his lack luster meal making. While take-out and prepackaged foods (convenience foods) may seem like a fast and easy alternative for Deniz, the health trade-offs are considerable. Perhaps joining an online cooking course, offered via the virtual assistant’s platform, may bring him back to healthy eating and also support his intellectual health. A well-known requisite to optimal physical health and mental well-being is ensuring we are properly nourished. People who cook at home eat a healthier diet, because unlike fast foods, home cooked meals result in lower consumption of calories, carbohydrates, fat, and sugar (Wolfson and Bleich 2015). Notably, “people who cook infrequently may benefit from cooking classes, menu preparation coaching or even lessons in how to navigate the grocery store” (Wolfson and Bleich 2015; JHBSPH 2014).

4.3

Persona, Scenario, and Solutions

31

Consequently, an online cooking course will provide Deniz with generalized nutritional supports (e.g., access to recipes and their supporting grocery lists), but in so doing, it will also provide him an opportunity for intellectual stimulation—to grow his intelligence, sharpen his skills, and enhance his knowledge. Not to mention, by his joining online beginner cooking classes, who knows, after having learned some new skills and recipes, cooking might just become Deniz’s new hobby. Advancing the Supportive Virtual Assistant. The ways in which we interact with these assistants have changed over time. Initially requiring physical interactions, digital assistants needed us to type into our keyboards and touch the device to make things happen (e.g., PalmPilot). We then moved to voice command virtual assistants, where we used our voice, through smart speakers, to talk to computers (e.g., Amazon Echo). The next iteration is ambient technology, where the technology operates in the background and does the work on your behalf. AI advancements have eased setup, where once we would have to program scripts to create smart home commands (e.g., turn lights on when we enter a room, turn those lights off when we leave the room), new out of the box AI enables us to quickly select our routines (e.g., smart lights slowly turning on in your bedroom to wake you) all from a simple app that connects all our home devices (lights, TVs, appliances, etc.) to the Internet and to each other. Ambient technologies allow these virtual assistants to fade into the background of our everyday life. While we have shared a few simple ways in which virtual assistants can support healthy aging in place, even more supportive enhancements will be available with further innovation. Currently, the microphone is the key interface for voice-recognition software. When we ask “Alexa, turn off the living room lights,” it is the microphone that picks up our voice and sends the sounds to our home-hub. The home-hub then transfers the digital voice data to Amazon cloud servers, where the words (the request) are processed and sent back to the home, via the hub, to perform the requested action: actually turning off your living room lights. Similarly, to support our health and well-being further, new innovations will be able to recognize anomalies. For instance, if Deniz normally calls his daughter, Dilara, at least once over a 7-day period, if on day eight Deniz has not called Dilara, the virtual assistant could ask “Deniz, did you want to call Dilara today?” Inevitably, ICTs will continually evolve and adapt, therefore, it is paramount that industries include aging populations in their research design to ensure the technology is senior-friendly and relevant. And importantly, we need to continue to increase older adult skills in using the technology, thereby requiring stakeholders create age-appropriate training programs.

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4.4

4 Community-Dwelling Adults: Aging Well at Home

Summary

The large majority of older adults are living physically and cognitively healthy lives— facilitating their ability to age in place. However, they may not all be as engaged physically or socially, as would be ideal for their well-being. Because aging is inevitable, we do well to contemplate early what we can do, and what is available to us, to live safely and on our own terms. ICTs can help us in supporting our eADLs across our lifespan and virtual assistants can be a supportive solution for older adults looking for new ways to live happier, healthier, and more fulfilled lives.

4.5

Find Out More

There are many popular YouTube channels that promote senior-friendly programs (e.g., resistance and strength training, indoor walking routines, or chair yoga) to help older adults live active lifestyles. https://www.youtube.com/. The Wellness Institute delivers virtual, personalized nutrition and physical training https://wellnessinstitute.ca/online-coaching/ along with free wellness webinars and e-challenges https://wellnessinstitute.ca/community-events/. Amintro is a free social platform for adults 50 plus, aimed at friendship-making and combating social isolation https://amintro.com/. The second book in the AGE-WELL series: Autonomy and Independence: Aging in an Era of Technology https://link.springer.com/book/10.1007/978-3-031-03764-1#: ~:text=Back%20to%20top-,About%20this%20book,adults’%20social%20connections% 20and%20environments.

References Berg-Weger M, Morley JE (2020) Loneliness in old age: an unaddressed health problem. J Nutr Health Aging 24(3):243–245. https://doi.org/10.1007/s12603-020-1323-6 Booth FW, Roberts CK, Laye MJ (2012) Lack of exercise is a major cause of chronic diseases. Compr Physiol 2(2):1143–1211. https://doi.org/10.1002/cphy.c110025 Buchman AS et al (2012) Total daily physical activity and the risk of AD and cognitive decline in older adults. Neurology 78(17):1323–1329. https://doi.org/10.1212/WNL.0b013e3182535d35 Halloway S et al (2020) Interactive effects of physical activity and cognitive activity on cognition in older adults without mild cognitive impairment or dementia. J Aging Health 32(9):1008–1016. https://doi.org/10.1177/0898264319875570

References

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JHBSPH (2014) Study suggests home cooking is a main ingredient in healthier diet. Johns Hopkins Center for a livable future: Johns Hopkins Bloomberg School of Public Health. https://clf.jhsph. edu/about-us/news/news-2014/study-suggests-home-cooking-main-ingredient-healthier-diet. Accessed 27 Mar 2023 Kakulla B et al (2021) video chats help bond grandparents and their young grandchildren, AARP. https://doi.org/10.26419/res.00468.001. Accessed 26 Mar 2023 Krell-Roesch J et al (2021) Lack of physical activity, neuropsychiatric symptoms and the risk of incident mild cognitive impairment in older community-dwelling individuals. Ger J Exerc Sport Res 51(4):487–494. https://doi.org/10.1007/s12662-021-00732-8 Maher CA et al (2020) A physical activity and diet program delivered by artificially intelligent virtual health coach: proof-of-concept study. JMIR Mhealth Uhealth 8(7):e17558. https://doi.org/ 10.2196/17558 Morr M et al (2022) Chronic loneliness: neurocognitive mechanisms and interventions. Psychother Psychosom 91(4):227–237. https://doi.org/10.1159/000524157 Ng R et al (2020) Smoking, drinking, diet and physical activity-modifiable lifestyle risk factors and their associations with age to first chronic disease. Int J Epidemiol 49(1):113–130. https://doi. org/10.1093/ije/dyz078 O’Rourke HM, Sidani S (2017) Definition, determinants, and outcomes of social connectedness for older adults: a scoping review. J Gerontol Nurs 43(7):43–52. https://doi.org/10.3928/0098913420170223-03 Paay J et al (2022) Can digital personal assistants persuade people to exercise? Behav Inf Technol 41(2):416–432. https://doi.org/10.1080/0144929X.2020.1814412 Rogers WA, Mitzner TL, Bixter MT (2020) Understanding the potential of technology to support enhanced activities of daily living (EADLs). Gerontechnology 19(2):125–137. https://doi.org/10. 4017/gt.2020.19.2.005.00 Roubenoff R (2007) Physical activity, inflammation, and muscle loss. Nutr Rev 65(suppl_3):S208– S212. https://doi.org/10.1111/j.1753-4887.2007.tb00364.x Sharifian N et al (2022) Social relationships and adaptation in later life. Compr Clin Psychol 2022:52–72. https://doi.org/10.1016/B978-0-12-818697-8.00016-9 Stoewen DL (2017) Dimensions of wellness: change your habits, change your life. Can Vet J 58(8):861–862. https://pubmed.ncbi.nlm.nih.gov/28761196/ Victor C et al (2000) Being alone in later life: loneliness, social isolation and living alone. Rev Clin Gerontol 10:407–417. https://doi.org/10.1017/S0959259800104101 WHO (2020) Basic documents 49th edition, World Health Organization Governance. https://apps. who.int/gb/bd/. Accessed 27 Mar 2023 WHO (2022) Non communicable diseases. World Health Organization Newsroom. https://www. who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Accessed 27 Mar 2023 Wolfson JA, Bleich SN (2015) Is cooking at home associated with better diet quality or weight-loss intention? Public Health Nutr 18(8):1397–1406. https://doi.org/10.1017/S1368980014001943 Yoneda T et al (2021) The importance of engaging in physical activity in older adulthood for transitions between cognitive status categories and death: a coordinated analysis of 14 longitudinal studies. J Gerontol: Ser A 76(9):1661–1667. https://doi.org/10.1093/gerona/glaa268

5

Sensor Technologies: Collecting the Data in the Home

5.1

The Challenge

Human resources are expensive. If you really want to keep your house safe, you should hire security guards to watch your house 24/7. However, most of us cannot afford that kind of staffing. The next best thing, then, is to install a security system. Essentially, we would buy a set of sensors that provides a version of ‘watching’ the house. We may buy sensors for the doors and windows—since we do not want them to be opened while we are away. These sensors only have a single function: “Is the door open or closed?” We could also get a system that includes cameras installed throughout the house and perimeter. These will provide us with much larger quantities of data and more precise information. When considering which types of sensors to install, we need to consider their advantages and disadvantages. While sensors can’t truly replace security guards because we still require a human to investigate a sensor alarm, the sensors provide the opportunity for fewer humans to ‘guard’ more homes; hence, improving cost-effectiveness. Which sensor can do the job. There are a number of considerations to be made when choosing which type of sensor is best for a particular application: precision, cost, and quantity. Say, for example, we are considering whether to buy a door sensor or a video camera for the front entrance, one consideration might be: “What type of information are we hoping to gain from the sensor?” If we choose a door sensor, this sensor will let us know if the door is open or closed, while a video camera will produce data that can be converted to information on who or what approached a door and what they/it did. Another question might be: “How much data will this sensor generate?” Our door sensor will provide very small amounts of data (door is open/closed), while our video camera will produce large volumes of data (for every second it is recording). A further question might be: “What is the budget for our purchase?” The door sensor, built with a magnet and switch, is going to be cheaper than a video camera. Typically, each sensor connects to a

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 F. Knoefel et al., Supportive Smart Homes, Synthesis Lectures on Technology and Health, https://doi.org/10.1007/978-3-031-37337-4_5

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central hub device (capable of supporting many sensors), which is in turn connected to the Internet. Therefore, these hubs, along with the internet service, also carry individual costs. Each of these system aspects will be covered in Chap. 6. Additionally, an intimate and wholly individual consideration surrounds the notions of privacy. As will be discussed in Chap. 12, different individuals will weigh the costs/benefits on the level of security versus the invasion of privacy differently, leading them to select sensors which may convey more or less personal information. A “door open/closed” sensor does not provide personal information, because while it may have a serial number to provide information to those who will provide the assistance, others should not be able to easily identify where a particular sensor is located (the exact street address). But even if the address is known, it should not be easy to identify which door the sensor is attached to (basement door, fridge door). Video, on the other hand, can be used to identify exactly where the cameras are located and the identification of persons or things within viewing distance. However, aging in place requires much more than simply a home security system, therefore, our sensor choices also need to consider technologies that provide necessary supports for aging adults. Home support. In general, older adults prefer to live and be cared for in their homes within the community, where their care is typically supported by family members and home care providers, and not in retirement or long-term care homes. However, not everyone has family living close by and the home care sector is already stretched. This has been one of the drivers of the AgeTech sector. Once AgeTech researchers understand an age-related challenge, and before they run off to reinvent the wheel, it would be smart to ask if we can take existing sensors, developed by the security industry or other sectors, and repurpose them for use in homes. For instance, using existing security system motion sensors, can we identify when there ‘should’ be motion in the home even when there isn’t (e.g., older adult is not in the bathroom at their usual morning time because they have fallen)? Undoubtedly, setting up sensors to identify when someone is breaking into a house is a lot simpler than setting up supportive smart sensors to monitor and cue all the activities that older adults may be performing in their home (Part II of this book identifies areas that would benefit from sensor AgeTech). Before we start presenting actual sensor types that can be deployed in a supportive smart home, we will touch on sensor categorizations and why these categories are important. Cost. There are financial implications to consider when deciding which AgeTech will assist our ability to age in place. What is the actual cost of the sensor? Will the sensor also require the purchase of a hub so the sensor can connect to the Internet? Is there a cost to transmit and/or analyze the data? Will you install the sensor yourself or will there be a cost to have it professionally installed? If you hire a smart home service provider, you’ll likely purchase a contract wherein you may not have to pay upfront for the sensor, the hub, or accessing the Internet (similar to a home security contract). Or, perhaps you pay a large one-time service fee when you buy an off-the-shelf home system, which includes

5.1 The Challenge

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internet storage space, but then you need to program the entire system yourself. One way or another, end-users will have to pay for the technology and its use. Perhaps in the future, if supportive smart home systems are considered a public good, governments will be willing to financially assist low-income households’ access, and potentially even for all citizens. Power. How power is provided to a sensor is important because (i) batteries need to be changed and (ii) plug-in power only works if a power outlet is available where the sensor needs to be placed and there is no power outage. We will now provide a few extreme examples of why power matters. In the case of homes with battery powered sensors, there will come a time when the batteries need to be replaced. A good system will indicate that the battery for a particular sensor is low before it ceases to function. For instance, a smoke detector emits a beeping sound when the battery needs to be replaced. If a home has 20 sensors with batteries that last 12 months each, it is unlikely that they will all require replacement around the same time. Thus, in a worst-case scenario, a system that has sensors requiring batteries to be replaced will begin beeping in the middle of the night 5 times every 90 days. Fortunately, battery lives are getting longer and the sensors need less electricity from these batteries. On the other hand, a plugged system will work continuously until there is a power failure. In the case of a power failure, for instance during a severe thunderstorm, it is still possible that the occupant will wake up that night. This could result in significant consequences if the occupant is expecting the sensors to still be functioning. Without power, the occupant will have difficulty seeing in the home (because their lights are not automatically turning on), resulting in an increased likelihood of tripping over something and falling. Also, a system based on plugged sensors will not detect a bed exit nor movement within the home, and will not notify formal or informal care providers on the night they are most needed. Thus, as we design systems to handle important information, the balance in the use of wired and/or battery power needs to be considered. Data Generated. The amount of data produced by each sensor is important because of data transmission and storage challenges. These will be discussed in more detail in Chap. 6. The accuracy of sensor produced data and the interpretation of this information also matters. For example, false alarms annoy people and typically result in the system being turned off by the resident(s). To demonstrate, let us go back to a door sensor that is part of a home security system. If we are on vacation and a burglar opens the back door, an alarm will go off and police will be dispatched to our home. However, if the door sensor is attached to a screen door, and every time there is a windy day the screen door is blown open, the police may begin to charge the home owner for visits based on false alarms. The homeowner might then simply disconnect the back door sensor to avoid paying fines, which in turn, provides the burglars with a good entry point into the home. Similarly, a sensor may not be installed correctly, for instance, the two parts of the sensor are placed too far apart. This could have the sensor indicate that the door is opened even

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if it is not. Again, not having the right information can cause the whole system to not be trusted. Privacy. For obvious reasons, privacy matters to people. This chapter will address privacy related to specific sensors, but given the importance of the topic, we devote a large section to privacy concerns in Chap. 12.

5.2

What’s in This Chapter?

This chapter will discuss the key advantages and disadvantages of various types of sensors used in a supportive smart home. Advantages and disadvantages will be explored in: • The cost of the sensor; • The type of power used and lifespan if battery powered; • The quantity of data generated, since this will affect data storage and/or processing power/speed required to analyze and manage the data; • The accuracy of the information provided; • The risks with regards to privacy; and • An example of the technology.

5.3

How to Select a Sensor

The approach for this chapter will be as though you, the reader, were shopping for a sensor and you were using this book as an aid in selecting the best sensor for your needs. For the sake of organization, the first division will be by cost (low-, medium-, high-cost), and wherein we will address each of the other categories. The sensor costing scale has been divided into low-cost sensors (75%) wish to stay in their homes as they grow older (Binette 2021). To achieve this, we will need to develop methods to support independence in the home. As the Baby Boomer generation ages, there will be more older adults who need support, but fewer human resources to help with this care. To meet this challenge, technological solutions will have to be developed to complement current methods of care delivery in the community which primarily rely on help from informal caregivers, formal caregivers, or other healthcare professionals. Supportive smart home solutions will therefore play a significant role in the solutions aimed at helping individuals to age in the place of their choosing. For a larger perspective on the concepts of autonomy and independence, please see the second book in the AGE-WELL series, Autonomy and Independence: Aging in an Era of Technology, by Liu et al.

10.2

What’s in This Chapter?

This chapter will explore the challenges that older adults face in maintaining their ability to perform activities of daily living. Challenges related to functional decline impacting the daily lives of relevant personae will be reviewed and then potential sensors and analytic solutions will be considered. Potential supportive smart home solutions for monitoring and supporting ADL performance will be reviewed.

10.3

Personae, Scenarios, and Solutions

10.3.1 Alfred Ziegler Persona. Alfred Ziegler is a 78-year-old male widower who lives alone in a bungalow in Bern, Switzerland. He suffers from severe osteoarthritis and has had both knees replaced. He also has a long history of diabetes that has not always been well controlled. After his wife died, he was able to compensate and started cooking simple meals. He performs daily house chores, and his family comes by to help with larger cleaning tasks every week.

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Personae, Scenarios, and Solutions

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Scenario. Although he was initially managing fairly well, Alfred has experienced progressive difficulties with some personal care tasks due to pain and arthritis. He also has more difficulties with fine motor tasks involving his hands stemming from arthritis and declining sensation due to nerve damage from his diabetes. This is now causing him to have problems doing up his buttons and zippers. He is wearing more casual clothing, that is easier to put on, but still finds dressing more difficult and sometimes does not bother to change his clothes. Showering has also become more difficult because of issues with balance and challenges getting in and out of the bathtub. Although he tells his family that he doesn’t care what he is wearing because he lives alone, inattention to his personal hygiene could have more serious consequences if not addressed. With diabetes, he is at risk of foot ulcers, and these may be missed if he is not changing his clothes and socks regularly. This can have serious consequences for his health because small ulcers can become infected, and if not treated properly he is at risk for requiring amputation of his toes, feet, or lower legs. His family has tried to set up care with a formal caregiver (e.g., personal support worker) but he has so far refused to let someone else help him with these more personal tasks. His daughter, Monika, is now wondering if there are technological solutions that could help them to monitor his daily activities. Monika heard about a study using home sensors to detect and classify different activities of daily living at the University of Bern (Nef et al. 2015). Solution. Monika is worried that Alfred will continue to neglect his personal care because of his physical limitations. She has no way to know how routinely Alfred is bathing or changing his clothes. Alfred is more receptive when Monika talks to him about hygiene. When she visits, with a bit of encouragement, she can convince Alfred to put on new clothes. Although Monika is there regularly to help her dad, she cannot visit every day. Monitoring activities related to personal care can be more difficult because of the need to maintain privacy. More intrusive sensors, such as cameras, would not be appropriate for assessment of activities that occur in the bedroom or bathroom. A potential solution

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to this problem is to make use of sensors that provide indirect measurements related to specific activities. Work in this area is currently in early stages, but the evaluation of sensor substitution (see Chap. 11), the use of sensors for applications different than their intended use, could be a promising method for monitoring ADLs using multiple low-cost sensors in the home. For shower use, a temperature sensor could detect changes in the temperature of a pipe to indicate when hot or cold water is being used and for how long (Forster et al. 2022). This solution makes use of an indirect measurement of water flow and also maintains privacy. Sensors that detect water flow directly are expensive to install as they require the sensor to be inserted in a water pipe (see Chap. 5, Sect. 5.3.3). The use of this low-cost sensor, to detect water temperature, could help identify when Alfred is using the shower. Another low-cost sensor that could be used to determine shower use is a humidity sensor. By providing his family with this information, they can be aware of the frequency of his showering (Fig. 10.1). If needed, a family member could provide a gentle reminder when they are visiting for him to have his next shower. Other aspects related to personal care can be detected with unobtrusive sensors in the bathroom as well. Motion sensors can detect when Alfred is in the bathroom to see that this is occurring at regular and expected times, such as in the morning for personal care and in the evening when he is getting ready for bedtime. Contact sensors, on the drawer that contains Alfred’s toothbrush and toothpaste, could detect opening and closing of the drawer, suggesting the regularity that oral hygiene practices are being done. Finally, a moisture sensor placed in the toilet water tank could indirectly monitor use, i.e., the frequency of flushing of the toilet. Regular monitoring of daily activity performance can help reassure Alfred’s family that they will be able to detect if he is neglecting certain aspects of his daily care. Smart home systems using multiple types of sensors placed in multiple locations around the home are being used to detect daily activity patterns and form a baseline of an individual’s normal routine. In a supportive system, a suite of smart home sensors that collect data from multiple modalities (e.g., motion, sound, temperature) can be used to more precisely identify the timing and pattern of performance of specific iADLs and bADLs (Urwyler et al. 2017; Dawadi et al. 2013). These systems use multi-sensor data fusion (see also Chap. 6, Sect. 6.4.2) to identify the presence of an activity in the home and classify it into different activities of daily living (e.g., cooking, eating, grooming, seated activity, sleeping, toileting, watching TV) (Nef et al. 2015). Work in this area is being done with different populations of older adults with chronic medical conditions, such as frailty (Dupuy et al. 2017), Parkinson’s disease (Botros et al. 2019), and congestive heart failure (Saner et al. 2021). These systems are showing the ability to detect changes in activity patterns related to diseases that impact the ability to perform iADLs. For example, individuals with cognitive impairment show increased variability in their regular pattern of performing daily activities compared to healthy older adults (Urwyler et al. 2017). By installing multiple unobtrusive sensors around the home, a normal pattern of activity can be established for an individual, without them having to interact with the system. Ambient sensor platforms

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Personae, Scenarios, and Solutions

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Fig. 10.1 Personal hygiene helps to maintain good health and prevent germs from spreading. Image by zinkevych on Freepik

are more likely to be tolerated by older individuals due to their passive collection of data. Individuals do not need to remember to put on a wearable device, learn how to use any equipment, and can just continue to go about their daily life as usual. Changes in their daily patterns can be detected that reflect changes in performance of specific activities. If individuals or their families are alerted to a decline, then resources could be implemented sooner to help support ADL performance and maintain their ability to stay at home.

10.3.2 Robert Brown Persona. Robert Brown is a 75-year-old male living in Timmons, Ontario, Canada. He was recently diagnosed with mild cognitive impairment (MCI). He is managing his daily activities, however, due to his cognitive impairment, his license was revoked, and he is no longer able to drive. After his retirement he continued to be active, meeting up with his friends regularly at the Royal Canadian Legion, attending his weekly poker game, and swimming at the local pool a few times a week. Since he lost his license, his friends

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and family have been very supportive. His friends check on him regularly and help drive him to some activities. However, they are noticing that he is not always ready when they arrive at the time decided upon the day before. He is accepting of the help but is very frustrated with his loss of independence. He often tells friends that he does not want to be a burden on them and sometimes declines to attend an activity if he is not encouraged to come out.

Scenario. Robert continues to enjoy social activities, but his friends have noted a few occasions where he did not arrive on time to an activity and when they called him, he only recalled that he had missed the event after speaking with them. Robert has always kept an agenda to organize his daily schedule, but he sometimes forgets to check what activities are coming up. As his memory symptoms progress, his friends are worried that he will miss more events, and this will result in increased social isolation. This can be due to not only forgetfulness but also to a decline in motivation and self-activation related to apathy—which can be related to cognitive impairment. Not only can increased feelings of loneliness increase his risk of further cognitive decline (Sutin et al., 2020), but there can be negative consequences for his mental health, with loneliness and social disconnectedness being associated with higher levels of depression and anxiety symptoms (Santini et al. 2020). Solution. Providing external cues can improve the ability of people with cognitive impairment to perform ADLs, including keeping appointments (Wilson et al. 2001). Prior work has used notifications sent to a pager (Wilson et al. 2001) or smartphones (Jamieson et al. 2022; Schmitter-Edgecombe et al. 2022), but this requires an individual to keep a device with them at all times for the notifications to be received. Robert’s family has

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Personae, Scenarios, and Solutions

119

already set him up with a smart speaker in the home, to easily listen to music and video chat with family, but it could also help with management of his daily activities. The speaker can act as a scheduling assistant and provide auditory reminders for planned activities. Relying on a physical agenda or calendar can be more challenging for an individual with memory impairment, since they are more likely to forget to check their schedule or misplace an agenda book. Robert would need assistance to set up and maintain his calendar of activities using an electronic format and the smart speaker, but this could be supported by a friend or family member. Although Robert will still require help with his calendar, a friend would only have to review his calendar intermittently, and his family and friends will then not have to worry about checking on him daily to ensure nothing is missed. Other activities of daily living that serve as indirect measures of social activity, such as computer and telephone use (Austin et al. 2016; Petersen et al. 2016), can also be monitored through smart home technologies (see also Chap. 5 Sect. 5.3.2). Although this type of assessment seems very invasive to an individual’s privacy, information about socialization is examined only through patterns of computer and telephone use, without needing to record anything related to the content of the telephone call or specific details of what is being done on the computer (Fig. 10.2). For computer use, the number of

Fig. 10.2 Use of smart speakers and virtual assistant. Photo by Peter Kindersley on Centre for Ageing Better

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sessions in a day, the amount of time for each session, and the timing during the day of the sessions, can establish a pattern of use. For telephone use, the number of incoming and outgoing calls and the time spent on the phone can be monitored. Changes from an individual’s baseline usage of either the telephone or computer could then help detect signs of increasing or decreasing socialization. Different usage patterns for the computer have also been observed between individuals with MCI and healthy older adults, providing another potential method to detect cognitive changes (Kaye et al. 2014). This shows how computer and telephone use are both examples of more complex iADLs that could be monitored by supportive smart home technologies and provide important information on multiple outcomes (functional abilities, cognition, social activity). Using technologies supported by a supportive smart home could also help increase the connections for an older adult living alone. Although it cannot replace an in-person visit, video chat can help to reduce feelings of isolation (Kakulla et al. 2021). Setting up devices, like an Echo Show or Google Nest hub, can make it easier to initiate a call to family so that an individual does not have to figure out how to log in and set up a video call on their tablet or computer. One of Robert’s children has moved out of town and was not able to visit as much during the pandemic. The weekly video calls with his new granddaughter quickly became a highlight of the week for Robert. This also helped to lift his spirits when many of his activities were on hold due to pandemic restrictions. He was excited to have stories to tell his friends and bragging about his granddaughter’s milestones helped him as he began to return to his social time at the Legion and poker games.

10.3.3 Sunita Kumar Persona. Mrs. Sunita Kumar is a 70-year-old female living in New Delhi, India with her son and daughter-in-law. She suffers from severe degenerative disc disease in her lower back. They have noticed some increasing forgetfulness over the past couple years, but she has refused to have any assessment of her memory, telling them that she is just getting a bit older.

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Scenario. Sunita continues to cook regularly and is often home alone when she does this. She is able to prepare traditional family meals from memory without any problems. However, her son, Ashok, recently noticed that a burner was left on when he went into the kitchen for dinner. In addition, she burnt a dish in the oven once while she was watching TV and forgot to return to the kitchen before noticing the smell. They are also not sure if she occasionally misses her midday meal as they do not always see evidence that she has eaten when they return home. It is very important to Ashok that she remain at home and be supported by family, and they do not have additional funds to consider private help or assisted living. In addition to her cooking with possible cognitive impairment, Sunita’s meal preparation abilities are impacted by physical symptoms related to her back pain. She is not able to stand for too long when preparing meals (Fig. 10.3). Because of this, she may leave the kitchen and go sit down to rest her back in a chair in their living room. This increases the risk she may forget to return to the kitchen and attend to a pot on the stove. Solution. The most important issue to address for the Kumar family is kitchen safety, due to the severe consequences of a fire or issue with the stove. Being able to detect whether the oven or a stove top element is turned on is not sufficient to determine safety problems because these may be left on for a significant amount of time based on regular use for cooking certain items. By using smart, automated methods to analyze thermal sensors detecting stove-top use, dangerous situations and behaviors can be detected before a fire occurs (Yuan et al. 2012). For homes that use a gas stove, sensors to detect methane and natural gas (Hsu et al. 2019) could let the family know if a gas burner was left on or if there was gas leakage from the stove (see also Chap. 5, Sect. 5.3.3). The use of this system would provide reassurance to the Kumar’s and help Sunita remain independent for meal preparation, despite minor issues with forgetfulness. A supportive smart home

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Fig. 10.3 Preparing meal. Image on CANVA Pro

system can be set up with both auditory alerts for Sunita as well as an alert sent to a family member via cellular phone. Additionally, food safety can also be an issue for individuals with cognitive impairment, or persons who have lost their sense of smell, and they may not realize that food has spoiled. A solution for the kitchen would be an electronic nose that is able to detect bacteria and food spoilage (Green et al. 2014, 2011, 2009). This type of sensor could be integrated into a supportive smart home system to provide alerts to Sunita and her family, so that food is disposed of before they leave for the day (see Chap. 11, Sect. 11.2.1). Sunita’s family would also like to know if there are changes in her ability to put together recipes. Different types of home sensors have the potential to monitor for changes in performance of kitchen activities. The detection of kitchen activity has been studied using a Red–Green–Blue-Depth (RGB-D) camera (Lei et al. 2012), which provides information on distance and color, providing more fine-grained details. This type of sensor can detect the location of the hands and identify kitchen utensils being used during cooking, recognizing the normal actions performed during the preparation of a recipe. Algorithms analyzing the cooking process in real time can then detect errors, or a decline in cooking ability, and eventually provide prompts if a step is missed. However, the use of a camera can be considered too invasive for some individuals. Less obtrusive methods to monitor kitchen activities could provide information of kitchen usage with the placement of contact sensors on relevant cabinet doors and the refrigerator door. In addition to the

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Key Initiatives

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safety application, thermal sensors could detect regular use of the stove. The information from multiple different sensors can be analyzed to identify patterns that are related to meal preparation and eating. In this case, a baseline of normal kitchen usage could be established, then the Kumar family could be alerted if significant changes are detected (missed meals, missed ingredients in typical meals, use of the kitchen at irregular times) when they are away from home.

10.4

Summary

In this chapter, we have reviewed how challenges related to a decline in the ability to perform ADLs could be addressed using supportive smart home technologies. Supportive smart home systems offer potential solutions to support the performance of more complex iADLS and also monitor simpler bADLs. Normal daily activity patterns can be established and variations in these patterns can be detected by algorithms analyzing home sensor data. Having a better idea of what is happening on a day-to-day basis will allow for earlier interventions to support independence when declines in ADL performance are noted. Furthermore, smart home technology could also support social activities outside of the home by helping with scheduling of these activities and connecting older adults with family and friends that live at a distance.

10.5

Key Initiatives

COOK—Culinary Assistant Nathalie Bier and Sylvain Giroux, AGE-WELL researchers in Montréal and Sherbrooke, Québec, Canada, have spent the last decade working on COOK-Culinary Assistant, as part of the DOMUS Research Centre at l’Université de Sherbrooke. Originally working with adults with traumatic brain injury living in a group setting, they are now adapting the technology to help older adults living with dementia. There are four main components to their work, where they (1) developed a prototype, (2) tested it in a residential living facility, (3) assessed its success with the residents, and (4) have been improving it ever since. They have an interdisciplinary team of researchers and students that are now looking to expand the program into a residence for older adults. https://domus.recherche.ush erbrooke.ca/recherches/projects/cook-assistant-culinaire/.

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Find Out More

For a more detailed discussion on autonomy and independence, the reader is referred to the second book in the AGE-WELL series, Autonomy and Independence: Aging in an Era of Technology, by Liu et al. https://link.springer.com/book/10.1007/978-3-031-03764-1#: ~:text=Back%20to%20top-,About%20this%20book,adults’%20social%20connections% 20and%20environments.

References Austin J et al (2016) Smart-home system to unobtrusively and continuously assess loneliness in older adults. IEEE J Transl Eng Health Med 4:1–11. https://doi.org/10.1109/JTEHM.2016.2579638 Binette J (2021) Where we live, where we age: trends in home and community preferences. AARP Home and Community Preferences Survey. AARP. https://doi.org/10.26419/res.004 79.001. Accessed 27 Mar 2023 Botros A et al (2019) Long-term home-monitoring sensor technology in patients with Parkinson’s disease-acceptance and adherence. Sensors 19(23):5169. https://doi.org/10.3390/s19235169 Dawadi PN et al (2013) Automated assessment of cognitive health using smart home technologies. Technol Health Care 21(4):323–343. https://doi.org/10.3233/THC-130734 Dupuy L et al (2017) Everyday functioning benefits from an assisted living platform amongst frail older adults and their caregivers. Front Aging Neurosci 9:302. https://doi.org/10.3389/fnagi. 2017.00302 Feng Y et al (2009) Validation of disability categories derived from Health Utilities Index Mark 3 scores. Health Rep 20(2):43–50 PMID: 19728585 Forster P et al (2022) Assessing activities of daily living by measuring residential water use with low cost thermistors. In: 2022 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, pp 1–6. https://doi.org/10.1109/MeMeA54994.2022.9856541 Gilmour H, Park J (2006) Dependency, chronic conditions and pain in seniors. Health Rep 16:21–31 PMID: 16646272 Green GC, Chan ADC, Goubran RA (2009) Identification of food spoilage in the smart home based on neural and fuzzy processing of odour sensor responses. In: 2009 annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 2625–2628. https://doi. org/10.1109/IEMBS.2009.5335374 Green GC, Chan ADC, Goubran RA (2011) Tracking food spoilage in the smart home using odour monitoring. In: 2011 ieee international symposium on medical measurements and applications. IEEE, pp 284–287. https://doi.org/10.1109/MeMeA.2011.5966685 Green GC, Chan AD, Lin M (2014) Robust identification of bacteria based on repeated odor measurements from individual bacteria colonies. Sens Actuators B Chem 190:16–24. https://doi.org/ 10.1016/j.snb.2013.08.001 Hsu W et al (2019) Application of internet of things in a kitchen fire prevention system. Appl Sci 9(17):3520. https://doi.org/10.3390/app9173520 Jamieson M et al (2022) Designing ApplTree: usable scheduling software for people with cognitive impairments. Disabil Rehabil Assist Technol 17(3):338–348. https://doi.org/10.1080/17483107. 2020.1785560 Kakulla B et al (2021) Video chats help bond grandparents and their young grandchildren, AARP. https://doi.org/10.26419/res.00468.001. Accessed 26 Mar 2023

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Kaye J et al (2014) Unobtrusive measurement of daily computer use to detect mild cognitive impairment. Alzheimer’s & Dementia 10(1):10–17. https://doi.org/10.1016/j.jalz.2013.01.011 Lawton MP, Brody EM (1969) Assessment of older people: self-maintaining and instrumental activities of daily living1. The Gerontologist 9(3_Part_1):179–186. https://doi.org/10.1093/geront/9. 3_Part_1.179 Lei J, Ren X, Fox D (2012) Fine-grained kitchen activity recognition using RGB-D. In: UbiComp ’12: proceedings of the 2012 ACM conference on ubiquitous computing. Association for Computing Machinery, pp 208–211. https://doi.org/10.1145/2370216.2370248 Nef T et al (2015) Evaluation of three state-of-the-art classifiers for recognition of activities of daily living from smart home ambient data. Sensors 15(5):11725–11740. https://doi.org/10.3390/s15 0511725 Petersen J et al (2016) Phone behaviour and its relationship to loneliness in older adults. Aging Ment Health 20(10):1084–1091. https://doi.org/10.1080/13607863.2015.1060947 Rockwood K et al (1999) A brief clinical instrument to classify frailty in elderly people. The Lancet 353(9148):205–206. https://doi.org/10.1016/S0140-6736(98)04402-X Rockwood K et al (2005) A global clinical measure of fitness and frailty in elderly people. CMAJ 173(5):489–495. https://doi.org/10.1503/cmaj.050051 Saner H et al (2021) Case report: ambient sensor signals as digital biomarkers for early signs of heart failure decompensation. Front Cardiovasc Med 8:617682. https://doi.org/10.3389/fcvm. 2021.617682 Santini ZI et al (2020) Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): a longitudinal mediation analysis. Lancet Public Health 5(1):e62–e70. https://doi.org/10.1016/S2468-2667(19)30230-0 Schmitter-Edgecombe M et al (2022) Partnering a compensatory application with activity-aware prompting to improve use in individuals with amnestic mild cognitive impairment: a randomized controlled pilot clinical trial. J Alzheimer’s Dis: JAD 85(1):73–90. https://doi.org/10.3233/JAD215022 Statistics Canada (2013) Ninety years of change in life expectancy. Government of Canada. https:// www150.statcan.gc.ca/n1/pub/82-624-x/2014001/article/14009-eng.htm. Accessed 26 Mar 2023 Sutin AR et al (2020) Loneliness and risk of dementia. J Gerontol Ser B 75(7):1414–1422. https:// doi.org/10.1093/geronb/gby112 Urwyler P et al (2017) Cognitive impairment categorized in community-dwelling older adults with and without dementia using in-home sensors that recognise activities of daily living. Sci Rep 7:42084. https://doi.org/10.1038/srep42084 Wilson BA et al (2001) Reducing everyday memory and planning problems by means of a paging system: a randomised control crossover study. J Neurol Neurosurg Psychiatr 70(4):477–482. https://doi.org/10.1136/jnnp.70.4.477 Yuan M, Green J, Goubran R (2012) Thermal imaging for assisted living at home: improving kitchen safety. J Med Biol Eng 33:380–387. https://doi.org/10.5405/jmbe.1271

Part III The Future of Supportive Smart Homes

Future of the Technology

11.1

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The Challenge

The proceeding chapters identified challenge areas which still need to be addressed—both purely technological ones and those to improve the measurement of clinical parameters. To summarize, the technological challenges include: • At the sensor level, there are challenges with precision, cost, and improving nonintrusiveness. • At the data transfer level there are ongoing challenges because of limitations in current hub capabilities and interoperability, and decisions regarding local versus central computing. • Cloud-based data analytics is still in its infancy, and it is unclear how data from thousands of homes will be analyzed in real time. • Systems using predictive machine learning algorithms still face limitations in their ability to accurately forecast patterns and detect anomalies. • The ‘supportive’ part of supportive smart homes is just developing: “Which is the best way to notify older adults of changes in their physical and cognitive abilities?” and “Who else can have access to this information and how will it be done securely?” There is another challenge not yet discussed regarding the integration of sensors into supportive smart home systems. The current supportive smart home will likely require a combination of sensors, hubs, data analytic tools, and applications, with each also likely to come from different manufacturers/producers. The software within these various devices will also need to be updated on a regular basis. Sometimes these updates requires the user to interact with their smartphone to apply the update. What if the user is not aware of the update, or is not sufficiently knowledgeable to perform the update? Not to

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 F. Knoefel et al., Supportive Smart Homes, Synthesis Lectures on Technology and Health, https://doi.org/10.1007/978-3-031-37337-4_11

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mention the times when a manufacturer’s update improves functionality for their device/ system but has a knock-on affect which limits their ability to interact with other connected devices/systems. There may also be times when a device manufacturer ceases operations or stops supporting their older generation device. Similarly, some application makers may go out of business or stop supporting an app. Most of us who use smart technology have experienced the sudden loss of a device’s or app’s functionality until we have updated the software or performed a hard reboot. Any one of these changes could render a part of the system unfunctional and could impact the ability of an older adult to live alone safely, especially if they have become accustomed to the system helping them. These will have tragic consequences if, for instance, the mobility monitoring system stops functioning for any one of the above-mentioned reasons and the older adult has fallen. On the clinical side, human behavior is very complex—with significant individual differences in physical and cognitive abilities amongst all aging adults. How will systems be able to identify what is and isn’t normal for any given person? More work needs to be done to determine which functions in activities of daily living are most important to monitor and how to monitor them in a cost-effective way. For instance, we have mentioned the act of taking medication to optimize health throughout the book. The definitive way to know if a pill has been taken would be to label it with a radioactive tag and then after a supposed ingestion do a nuclear medicine test looking for radiation in the stomach. But this would present many ethical and cost issues. On the other extreme, it would be least invasive to add a magnetic switch to the cupboard where medications are kept. This sensor is very cheap, and opening and closing of the cupboard door can be monitored at low cost and respecting privacy. However, we lack precision in this case. If the patient forgets to put the pills back in the cupboard—they may continue to take the pills while no longer opening the cupboard door—a false negative alarm (door not opened, but the pill was taken) would occur. Additionally, the cupboard door could also be opened because there are likely other items stored in the same cupboard—this will generate false positives (door opened, but no pill taken). And then there is another type of false positive: the cupboard is opened at 09:00, the pill is taken out of the container, the container is returned to the cupboard, but the person forgets to take the pill. And so there can be false negatives and false positives for sensors monitoring cognitive function, mobility, and other ADLs. Inherently, all supportive smart homes systems will have some false positives and false negatives—collectively we will need to determine which rates are acceptable and ensure our systems exceed those standards.

11.2

What’s in This Chapter?

This chapter will look at current work aiming to increase the potential to connect thousands of supportive smart homes while also being able to maintain and potentially improve the occupants’ health and wellness.

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To do so, we will focus mainly on (1) advances in sensor technologies, (2) connectivity (data transmission), and (3) analysis methods (data analytics and response action development).

11.2.1 Sensor Technology Advancements Some sensors, such as contact sensors and infrared motion sensors, have improved to a point where their function and price have been optimized. Others can still be substantially improved—either by increasing the specificity or decreasing the cost of the sensor. This section will now highlight some of these challenges. Thermal cameras do not produce normal ‘light-based’ images, but instead, measure the temperature at various points within the image. Thermal camera use has been described in previous chapters, for instance, around activities of daily living in the kitchen. Until recently, thermal cameras capable of providing high resolution temperature measurements were $10,000CAN each; well outside the price range to make them useful for a supportive smart home. However, in the last number of years, low-cost thermal cameras (a few around $100CAN) have come onto the market, and although still perhaps at a price point too high for widescale use, price drops should continue, enabling their future application within supportive smart homes (see Table 5.1 Sensor Summary). An interesting application for the use of thermal cameras would be to provide yet another way to measure respiration rate. The air we breathe is at room temperature when we inhale it. The air will then warm in our lungs, becoming much closer to body temperature when we exhale. The result is that this alternation of warm and room temperature air around the mouth and nose, could provide an ambient mechanism to measure respiration rate, including determining episodes of apnea (Mozafari et al. 2022). This approach would not depend on direct contact with the subject (in contrast to the current gold standard: chest bands). Another potential application for thermal cameras is in the kitchen to support safety in stove/oven use and provide more information around cooking and nutrition practices. While initially these cameras were too costly to make this viable, recent price reductions make this technology now feasible for supportive smart home use. A thermal camera capturing the top of a stove can detect if the stove has been left on or is at an unusually (or unexpectedly) high temperature (Yuan et al., 2013) (Fig. 11.1). An extreme reading could trigger an alert to an appropriate individual well before the situation becomes a fire. At the meal-making level, the various types of cooking (frying, sautéing, boiling, steaming, stir frying, etc.) are all very distinctive in how the food and pan change temperature as the food product transitions from a raw state to a cooked state. For instance, a ‘flip’ event has a characteristic where the hot side of the food is now turned up and exposed, while the colder top is hidden against the pan. Similarly, a stirring event would show foods with uneven heating getting mixed together, leading to a more uniform temperature. Those

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are but two cooking examples which could be analyzed using thermal cameras. This analysis, of thermal images of cooking at a stove, could provide an indication of cooking complexity and provide inferences about nutrition, and when an individual’s baseline ability to changes over time—indicating potential behavioral or cognitive changes. Shifting from making complex meals (using the stove) to less complex meals (microwaving packaged foods) could, for example, be a sign of cognitive or physical decline. A healthcare professional, formal, or informal caregiver with knowledge of this type of change in behavior would know to explore for a cause and perhaps provide an intervention before the cognitive changes actually cause harm. An electric nose is a low- to moderate-cost sensor currently in use, for example, they can be found in most homes’ smoke and carbon monoxide detectors. Essentially, electronic noses can be trained to detect the presence of any aerosolized chemical. Electronic noses are also widely used in airports to detect the transportation of illegal goods. Their use in the supportive smart home is currently experimental, but future use could detect neglected personal hygiene, spoiling of food, or other relevant smells (see also Table 5.1 Sensor Summary). Sensor substitution and interesting new sensor scenarios: Sensors are typically designed to measure one thing well and we typically purchase them for that purpose. For example, as the name suggests, temperature sensors measure temperatures, such as those used to determine if a person has a fever (thermometer), to determine if heat/cooling is required in a home (thermostat), or to decide if food is cooked (food thermometer). However, temperature sensors are a good example of a sensor that can be used to replace (or

Fig. 11.1 Researchers at the SAM3 ‘smart apartment’ test a thermal camera used to understand cooking complexity. Courtesy of AGE-WELL

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substitute) another sensor. One of the advantages of temperature sensors is that they are relatively inexpensive (less than 1 dollar). Sensor substitution research explores ways in which sensors can be used to measure things which they were not intentionally designed to directly measure. As a result of sensor substitution, temperature sensor applications have expanded our capability to assess many different activities done in the kitchen. One such example would be for refrigerator use. For instance, typically we would use a magnetic switch type sensor to see if the refrigerator door is opened. However, as a result of the open door, cold air exits the fridge and a temperature sensor located below the door would be able to sense this drop in temperature, and another located inside the fridge would sense the inflow of warm room air, resulting in a method to measure if the refrigerator is being accessed. This could be a proxy measure of nutrition. Another kitchen application of sensor substitution would be temperature sensors placed under the sink to measure water pipes for hot- and cold-water flow (Forster et al. 2022). Water plays a key role in many kitchen activities, whether as an ingredient in food, being used to wash and prepare food, as a drink or to make tea/coffee, or even to clean-up after a meal. Therefore, knowledge of when water is used and which water (hot/cold/ both), provides additional knowledge for fusion systems to understand kitchen activities. Typically to measure water flow, we would add flow meters to the water pipes. However, doing this at every sink and toilet would be very costly to purchase and install (cutting pipe, installing flow meter, sealing pipe). Alternatively, a cheap thermal sensor, requiring minimal installation, can provide a lot of the same information. A pipe, where the water has not been used recently, will be at room temperature. If the pipe goes above room temperature, hot water is flowing through it, while conversely, a drop to below room temperature suggests cold water is flowing. With temperature sensors on various water pipes throughout the home (kitchen, washroom, etc.), a supportive smart home system would be provided with data related to cooking and hygiene behaviors. This is currently not possible without using much more privacy invasive or costly options (such as video). To further demonstrate, flood sensors could be installed in a novel bathroom application. Typically, flood sensors are used to detect water where it is not supposed to be (e.g., on the floor beside a bathtub). However, this sensor can be ‘used in reverse’ to indicate when there is ‘no water.’ If used in the toilet tank, its usual state would be ‘flooded’ (meaning water in the tank) while after a toilet flush it would be ‘not flooded’ (meaning no water in the tank). This application could be used to measure regular toileting. Lastly, humidity and temperature sensors, in some combination, may be able to differentiate between the use of the bath versus shower, as each of these will affect the bathroom’s temperature and humidity levels in different ways. Another novel area for sensor substitution might be in the unobtrusive monitoring of vital signs, such as pulse rate. When the heart compresses, it pushes blood through the arteries and into the capillaries (the smallest blood vessels) of the skin, and when the heart is expanding to refill with blood for the next compression, the capillaries empty. This

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happens every time the heart beats, so about once a second for a 60 beats per minute (bpm) heart rate. Capillary blood has a red color so that with each heartbeat there is a very slight red shift in skin tone. This effect on skin tone is not visible to the human eye. However, techniques are emerging that apply advanced signal processing to video to determine pulse rate without any need to touch the person or to even be in the same room. As the COVID19 pandemic demonstrated, there is a need for remote health care. Enabling a physician to remotely assess a patient’s vital signs will be a key addition to the telemedicine model. For those people living with conditions which require ongoing assessment of vital signs, signal processing of video data provides an automated alternative, such as by obtaining pulse rate through a phone app or the computer’s webcam. Advanced image processing: Video cameras can provide huge amounts of information to obtain knowledge on a person’s well-being, but they come with significant intrusion to the person’s privacy. This fundamentally limits where and when they can be used, and in many cases, precludes them from even being considered. New technology advancements are ushering in novel uses for their application. Earlier we discussed the use of computers to analyze sensor data. Fortunately, their processing abilities continue to increase, while their physical sizes continues to decrease. As an example of improvements in processing power, today’s smartphone has processing capabilities millions of times higher than those on the Apollo 11 rocket that landed on the moon in 1969. The result is that we can now put a computer directly into a camera and process the video locally. This would mean, in our thermal camera example, we would be transmitting only the results (e.g., pulse rate) and not the actual video (showing the skin of the person in the image). The thermal camera could find the person(s) in the image, identify who they are (person 1, person 2), locate exposed skin, like on the forehead, and then use video analytics to measure heart rate for person 1 or person 2. Alternatively, the camera could just as easily identify the mouth and nose of person 1 and use video magnification to measure person 1’s respiration rate. These techniques could also be applied to light video using video magnification techniques. A typical digital video camera produces videos with 30 frames (pictures) per second. Emerging applications for these video cameras, with computers embedded within them, can look at each frame and use AI to look for human shapes (Agarwal et al. 2022a, b) (Fig. 11.2). The software can be taught who a home’s occupants are, and when it sees a person, it can identify them as person 1 or person 2. This way, transmitted data can be anonymous (person 1, person 2), rather than transmitting completely recognizable images of the people. Key here is that the system does not need to name them (i.e., Wilma vs Fred) but can provide an anonymous identification that qualified users of the system know. Even more interesting, AI can generate an 18-point stick figure of the body by determining the location of a person’s feet, knees, hips, hands, head, etc. The result, again, is the camera need not share video images of person 1, but rather transmit (a) the 18 points of person 1, and (b) the coordinates for the location of these 18 points taken from the image frame, as demonstrated in Fig. 11.2. Although no light images ever leave the

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Fig. 11.2 18-point Stick Figure, from camera with embedded AI, showing a person walking. Courtesy of SAM3

camera it may still be possible to distinguish between person 1 and person 2. By looking at the stick figure, in the case of a mobility assessment, we can follow the sequence of the stick figures to assess how they are moving. The result is a completely new way to measure activity and motion, such as walking speed, stride rate, stride symmetry, and many other aspects of gait. It could also be used to assess sit to stand mobility, along with many other body movement applications. Wi-Fi as a sensor: Wi-Fi has become almost ubiquitous with nearly every home having a home Internet router to provide a local Wi-Fi network for the occupants to connect their phones, computers, or supportive smart home systems. In and of itself, this network itself has the potential to be a sensor as people moving around the home will actually perturb the Wi-Fi radio signal. Aerial, a company in Montréal, Québec, Canada, is building a system that has a device that listens to Wi-Fi signals and detects changes in them. Wi-Fi signals are perturbed by different types of movements. A home with no movement will have a constant Wi-Fi signal—with no perturbations. Recent work has shown that Wi-Fi can be used to distinguish between the actions of standing up and sitting down (Joudeh et al. 2019, 2021) (see Table 5.1 Sensor Summary). Data fusion with other sensor sources: This book has explicitly excluded wearable sensors and social robots (Fig. 11.3), however, there is significant value in the well-being data that can be collected from these devices. Accelerometer data from a wrist-worn device would be able to add more precision (e.g., steps) to the monitoring of mobility. In fact, many such devices already have a feedback function: making a suggestion when a person has been sedentary for too long. Similarly, data from a social robot could be

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Fig. 11.3 Dr. Goldie Nejat (University of Toronto) with Casper, a socially assistive robot. Photo by John Hryniuk, Courtesy of AGE-WELL

used to assess the emotional and cognitive well-being of an older adult—which again could be combined with data from embedded sensors. For instance, if an older adult was limiting their conversations with their social robot and spending more time in bed, it may mean that their mood is declining. The future ability to bring together data from multiple sources will help improve the precision of the interpretation of the data. Redundancy and system monitoring: As mentioned at the beginning of this chapter, the integration of multiple devices from potentially multiple manufacturers/producers creates an integration challenge. Future home monitoring systems should be self-monitoring, for instance, regularly verifying if the connections to all sensors are intact. There needs to be redundancy built into the system, so that if one or two sensors fail (e.g., require a new battery, need software update) the system is still able to perform. Similarly, the system will need to be able to continue functioning during power failures or Internet connectivity issues. These issues will require hardware and software improvements as these are essential for system adoption and system safety.

11.2.2 Connectivity Future wireless evolutions: Wireless mobile telecommunication technology has improved significantly in recent years. Cellular technology was considered best for long-reach transmission, but its broadband performance was questionable. Wi-Fi technology with

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its higher broadband performance, was considered best for short-reach transmission. To illustrate, if we consider broadband performance, watching high-definition TV on your 3G cellular phone was limited or impossible, whereas watching it over Wi-Fi was relatively seamless. And, if we consider transmission reach, your cellular phone’s Wi-Fi will not work unless you are home or in a hot spot (e.g., coffee shop). With technological improvements, cellular performance is improving everywhere, and Wi-Fi is becoming more ubiquitous and robust—becoming interchangeable alternatives. Another significant limitation for both cellular and Wi-Fi technologies is that they are not conducive to direct use with sensors. This is because the transmission radios used in these two systems are too large, and require too much power, to allow a sensor to use them for any extended amount of time. The significant power requirements would result in the sensor’s batteries lasting days, at most. However, the evolution in cellular networks, will work to resolve and bridge the gaps. One of the key network design objectives for 5G is for sensor applications because its technology supports low-cost and low-power wireless data transmission. These changes would also reduce the need for a hub in each home and provide a platform for new sensor application models. The elimination of the hub represents cost savings, but the real advantage of moving to a 5G model is likely the advent of mobile devices. Sensors (both fixed at home and mobile ones), and the systems that support people, will work the same whether you are home, at work, out socializing, etc. Hence, people will receive the same supports while also being fully active and engaged in their community. The next evolution in wireless mobile telecommunication technology will focus on making wireless networks even more adaptable and agile. As wireless communication becomes more powerful the potential to have all sensors connect to one another, will provide new application opportunities within the supportive smart home space. With the necessary permissions, this technology will improve the ability to seamlessly monitor and care for people. This improved connectivity will also facilitate data fusion from multiple sensors, including wearable sensors and social robots, as discussed in Sect. 11.2.1.

11.2.3 Analysis Methods Data analytics—Machine Learning, AI, and new processing scenarios: Machine learning sounds like a complex topic, and to some degree, the software that makes it possible is very complex. But at the highest level, it is a very simple construct. We are asking computers to do what we humans already do instinctively. What is the foundational thinking behind machine learning techniques? One aspect of human intelligence is the ability for us to look for and see patterns in information. Pattern establishment is the foundation for most science. As a simple example, if you were to see a graph of the temperature for each hour, of each day, over a full year, you would likely be able to find the day/night pattern of warmer days and cooler nights. These

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patterns are subtle number changes in the data, but through human intelligence, we can see the patterns. Upon a higher-level overview of the data, the seasonal trend of warmer summers and colder winters would also be easily observed. The goal of machine learning is to provide computer software with raw data (temperatures for every day of the year) and allow it to look for and find patterns (warm days/cooler nights, seasonal trends). More importantly, computers, unlike humans, have the capability to deal with massive data sets without getting tired or bored looking for patterns. The reality for supportive smart home technology is that every home (physical structure) and its resident(s) is unique, and there is currently no way for an out-of-the-box supportive smart home system to be pre-programmed with all these different scenarios. Machine learning techniques allow a system to learn the patterns in a particular home, and then use this to form a definition of expected “normal” and “normal variation” within that home. This enables the supportive smart home to adapt to the occupants and deliver a personalized system. Response action development—even smarter homes—feedback and a supportive model: Currently, there is a transition from a monitoring home to a supportive smart home. An example of a supportive smart system can be as simple as having automated lighting in the hallway and bathroom to guide a person when they get out of bed at night. Since most nighttime awakenings are likely because a person needs to use the washroom, simple lighting provides them with a cue for their destination, which is very helpful in assisting an individual orient themselves. This will help decrease the chance of falling over an unseen object or the chance that a person living with dementia becomes disoriented when leaving the bedroom. This is a direct and first example where a supportive smart home provides feedback directly to the home’s occupants to support and enable their independence. The potential expansion for this type of support is vast, especially as homes get smarter and are able to help support in areas such as (a) nutrition, with reminders to prepare/eat meals when they are forgotten or missed, (b) well-being, with reminders to partake in activities for physical, cognitive, and social well-being, or (c) medication taking, with reminders when medications are forgotten, taken in duplicate, or at the wrong time of day. It could even be as subtle as controlling lighting within the home, to help with day/ night orientation—which can reduce nighttime exploring. By adjusting the color tone of the lights within the home, through the normal solar spectral shift of the day (sunrise, mid-day light, sunset), the system can provide cues to remain active during the day and stay in bed during the night, especially in facilities (e.g., retirement homes) where the hallways are lit all night.

References

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139

Summary

We have tried to convey that supportive smart homes are still in their infancy, yet, models to make ‘monitoring homes’ into ‘supportive smart homes’ have been suggested. We believe that the future is bright, but there is a lot of work that needs to be done. To get to a future of robust supportive smart home offerings, researchers and companies are trying to develop: • Better sensors: which generate better data, are cheaper, and have better physical forms. • Better transport of data: better hubs (until we don’t need them), better data transportation to the cloud. • Better analysis of data: improved cloud storage and algorithms. • Ways for the system to provide feedback to the older adults, or to their informal and formal caregivers, and healthcare professionals (as directed by the older adults). • System self-monitoring and redundancy to improve system usefulness and safety. The complexity of these endeavors will require constant reflection, using ethical principles, and the development of public policy that helps ensure personal data generated from homes is secure and private, and as these become essential to aging in place, there is redundancy built into the system so older adults can rely on their support.

11.4

Find Out More

AltumView stick-man camera sensor: https://altumview.ca/. Aerial WiFI as a sensor: https://aerial.ai/.

References Agarwal A et al (2022a) Method to improve gait speed assessment for low frame rate AI enabled visual sensor. In: 2022a IEEE sensors applications symposium (SAS). IEEE, pp 1–6. https://doi. org/10.1109/SAS54819.2022.9881252 Agarwal A et al (2022b) Walking gait speed measurement using privacy respecting AI enabled visual sensor. In: 2022b IEEE international symposium on medical measurements and applications (MeMeA). IEEE, pp 1–6. https://doi.org/10.1109/MeMeA54994.2022.9856484 Forster P et al (2022) Assessing activities of daily living by measuring residential water use with low cost thermistors. In: 2022 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, pp 1–6. https://doi.org/10.1109/MeMeA54994.2022.9856541 Joudeh IO et al (2019) WiFi channel state information-based recognition of sitting-down and standing-up activities. In: 2019 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, pp 1–6. https://doi.org/10.1109/MeMeA.2019.8802151

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Joudeh IO et al (2021) Location independence in machine learning classification of sitting-down and standing-up actions using wi-fi sensors. In: 2021 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, pp 1–6. https://doi.org/10.1109/MeMeA5 2024.2021.9478778 Mozafari M et al (2022) Respiration rate estimation from thermal video of masked and unmasked individuals using tensor decomposition. In: 2022 IEEE international instrumentation and measurement technology conference (I2MTC). IEEE, pp 1–5. https://doi.org/10.1109/I2MTC48687. 2022.9806557 Yuan M, Green J, Goubran R (2013) Thermal imaging for assisted living at home: improving kitchen safety. J Med Biol Eng 33:380–387. https://doi.org/10.5405/jmbe.1271

Next Steps in Ethics and Policy

12.1

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The Challenge

So far, we have presented some challenges associated with aging and examples where supportive smart home technologies could provide support to those wishing to age in place. However, we have only considered these in a simple one-dimensional way: here is a health challenge and here is a technology that may support it. In reality, there are other forces at play in complex environments and our societies, including ethical principles and unique policy issues in different jurisdictions. To illustrate, if we consider that some of these technologies are health-related, let’s think about the health environment in two neighboring countries: Canada and the United States of America (USA). Differences in how these countries view healthcare may impact how health policy includes supportive smart home technology. In Canada, visits to physicians, laboratory tests, and hospital admissions are paid for by provincial governments. Essentially, access to healthcare is considered a human right with a goal of equal access across the country. Healthcare policy is therefore written with that in mind. In the USA, the costs for healthcare services are the responsibility of the individual, with many, but not all, Americans having some kind of health insurance. In the USA, healthcare is considered more of a purchased service or a hybrid system, wherein most health care is delivered privately. USA health policy and access are very different from Canada, leaving more of the decision-making to individuals in conjunction with their employers—through health benefit plans or the private health providers they subscribe to. As supportive smart homes evolve, to provide information that could be used by the health care systems, the policies around this will likely look quite different in these two countries. And so, every jurisdiction (national, regional, local) will likely have its own unique policies to deal with supportive smart homes in the future.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 F. Knoefel et al., Supportive Smart Homes, Synthesis Lectures on Technology and Health, https://doi.org/10.1007/978-3-031-37337-4_12

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What’s in This Chapter?

In this chapter, we will try to address some of the complexities of the environments into which supportive technologies will become integrated. First, we will consider ethical principles that may apply in how we implement supportive smart home technologies. Then we will examine some of the ethical issues that are inherent to technology design. Finally, we will consider some public policy issues that arise from these. We will not attempt to describe how these ethical principles and policy issues would apply in any particular country, but rather, leave it to the reader to consider these issues in their home country. This chapter is an extension of previous work (Wallace and Knoefel 2022) published online by AGE-WELL as a white paper. It will also highlight findings, by various researchers in the AgeTech ethics space, presented at PETRA 2022: Pervasive Technologies Related to Assistive Environments conference (Chu et al. 2022).

12.3

Ethics

Ethical principles, when applied to real people, are rarely black and white. And ethical principles can overlap and sometimes pull in different directions. To explore these ethical issues let’s consider a hypothetical case study. Therefore, let us introduce a new persona: Julia Simpson.

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Persona, Scenario, Solution, and Ethical Principles

12.4.1 Julia Simpson Persona. Julia Simpson is a 75-year-old lady living with her partner, Susan, in a 2-story home in the suburbs of Toronto, Ontario, Canada. She has a Grade 12 education and worked as a bookkeeper for the City of Toronto for 30 years, then retired. She is treated for type 2 diabetes. Over the last 2 years, Julia has shown some cognitive decline. She is fiercely independent and is not very reliable at following a healthy diet (too many sweets, skipping meals) or recommendations concerning her beer consumption. She has had a number of falls recently, some associated with her arthritis and low blood sugar but she refuses to use a walking aid. And with the beer fridge being located in their basement, there is an increased risk to her falling. Susan does her best at reminding her wife what the doctors have recommended, but Julia does not always respond to Susan’s concerns in a positive manner. Susan tries to maintain her independence and social activities and is out three nights a week playing poker with friends.

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Scenario. Julia is at risk of falling because of her arthritis, and this risk increases when her blood sugars are too low or too high, or when she is consuming alcohol. Because of her desire to make her own decisions about her life, and some early cognitive impairment, she is at increased risk of falling or drinking when her wife is away. Given that Julia has fallen a number of times over the last three months, including once at the bottom of the stairs, Susan has done some research on AgeTech. She learned about supportive smart home systems which could help cue Julia while she is away, and it could also notify her when there is a possibility that Julia has fallen on the stairs. She is considering purchasing a smart magnetic switch to put on the beer fridge door, to ‘count’ the number of beers Julia takes, and a set of motion sensors for the top and bottom of the steps. Susan also plans to buy a smart speaker to be placed on top of the beer fridge, and connect everything to a supportive smart home system, that could provide notifications to Susan’s cellular phone. The system could be programmed as follows: each time Julia opens the fridge, it can provide an update: “Good evening, Julia, this is your third beer today.” Motion sensors could be situated as: one at the top of the stairs and one at the bottom, which would allow the system to learn the time it takes her to ascend and descend the flight of stairs. It could detect gradual changes in mobility related to worsening arthritis or a more acute change, such as related to consuming alcohol. It could detect a potential ‘fall down the stairs’ if her time on the stair is significantly shortened, or a ‘fall on the stairs’ if the second motion sensor is not triggered after the usual time. We will consider this case using traditional ethical principles.

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Beneficence and non-maleficence The system is supposed to provide a benefit (beneficence) to the user and not do harm (non-maleficence). This seems straightforward enough, but even this can become challenging in the case of supportive smart home technology and specifically the case of Julia. The intent of the system is good: help Julia to limit her drinking and thereby decrease her chance of a fall. And if she does fall, identify this quickly so help can come as soon as possible—another potential benefit. When we think of possible maleficence, the first could be ‘physical,’ for instance around the safety of the installation of the sensors. There could potentially be wires as part of the system, which could be a tripping hazard, especially the sensor at the top of the stairs. If the sensors don’t adhere properly to the wall, they could again become the cause of a slip and fall. A speaker on top of a fridge could fall off the fridge when trying to open the door, potentially injuring the person opening the door. In addition, there may be emotional maleficence. If Julia forgets that the system is in her home, it may upset her that a computer-generated voice is reminding her how many beers she has already had today. The voice may cause her to startle, drop the beer she is holding, causing the bottle to break, and potentially lead to cuts in her feet. Additionally, Julia may be quite aware of the system, but may not want the system to count the number of beers she drinks. She may then feel the technology is a harm to her sense of self. Maleficence at the societal level could also occur. If Julia is going down the stairs more slowly (to be safe) and the system triggers an alert that she may have fallen in the stairwell, this could potentially lead to an ambulance being sent to her home. This ambulance would then not be available to attend to another patient that is potentially having a heart attack—causing indirect harm to them. Finally, there may be maleficence if the technology is not working as it should (e.g., battery dead, internet down, power failure, algorithm issue). Since the person themselves, informal caregivers, and formal caregivers may be relying on the system, a system failure could cause harm. Hence, if Susan plays a few extra hands of poker, this could increase the morbidity to Julia if she has fallen and the system has not notified Susan (Fig. 12.1). Informed consent, autonomy, and capacity Another important ethical principle is informed consent, wherein we are all entitled to make our own informed, and uncoerced choices. This is important to the concepts of autonomy or independence. In the previous section, we showed that autonomy (ability to decide for oneself) can be at odds with beneficence (benefit of monitoring risk). The technology may be well-intentioned and even work perfectly, but the individual has the right to make their own decisions regarding risks. This practice of self-determination is also referred to as empowerment (Schicktanz and Schweda 2021). Case in point, most adults realize that alcohol consumption increases risks of falls, and yet, most adults are rightfully allowed to make the choice of how many drinks they are going to have. These concepts are then even more complicated within the context of capacity. Capacity is the

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Fig. 12.1 Fridge located in the basement of the house. Images on CANVA Pro

‘ability’ to make one’s own decisions. So, in the case of Julia, before we rely solely on her decision to install a ‘beer counter’ and verbal notification system, we may want to ensure that she does not have cognitive impairment interfering with her judgment. Furthermore, the argument could be made that even if she is capable to make these choices most days, she may no longer be capable after consuming four beers. Additionally, this case may also be further complicated by relationship dynamics. For instance, Susan may insist that if the ‘beer counter’ isn’t installed and activated she will empty the beer fridge every time she leaves the home (to indirectly reduce Julia’s risk of falls while she is away). Under these circumstances, Julia may choose to accept the ‘beer counter’ so that she has access to the beer. Is this type of coercion ethical? How will it affect their relationship? Finally, if it were determined that Julia was not capable, then Susan, her substitute decisionmaker, would make the technology decision on her behalf. However, there remains the question of assent—where Julia would have the ability to unplug or turnoff parts of the system that were bothering her. This distinction between informed consent (the ability to understand the important elements of a decision and then make a decision informed by that information), and assent (the ability to decide in real time what happens to a person), is an important one. Privacy Another important ethical concept is privacy: being able to control who has access to one’s personal information (Fig. 12.2). An obvious case, most people would not want video footage from a color video camera in their bathroom to be made readily available to anyone who wants to see it. In our example, Julia may be willing to have the system

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Fig. 12.2 Data privacy. Image by rawpixel.com on Freepik

remind her directly how many beers she has had, but she may not want this information to go to her wife. And while she may or may not want this information to go to her family physician’s office, if she did allow this, she would want it to get there securely, to ensure no one else could access her information. One of the first decision points for Julia will be regarding whom she will allow access to her supportive smart home information. Presumably, she will want access to all of her own data, which the system must be able to provide—in a useable format. Two components of privacy include security (to ensure only authorized people find and see this information) and anonymization (to make it more difficult to determine whose information is being seen). Therefore, security could require the use of complex passwords to access the data, and anonymization behind the security would only provide data about a sensor number and its status (e.g., open, closed.) This would make it very difficult to determine (1) the home address the sensor is located, (2) what type of sensor it is, (3) where it is attached (in this case a fridge), and (4) the name of the person activating that sensor. Honesty We often take honesty for granted. But is emphasizing the benefits of supportive smart home technology, and minimizing the risks to loss of privacy in its use, truly being honest? In this case, both fridge sensor and motion sensors have a positive intent and could reduce morbidity and possibly even mortality. And if there is strong security protection and anonymization, the risk of privacy breach should be low. So, what language would be considered honest? For instance, if an authorized supportive smart home salesperson were to say: “This system has a strong likelihood of reducing your chance of falling, and if

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you fall, reduce your injuries associated with the fall. It is highly unlikely anyone will be able to access this information.” Do we know what ‘strong’ and ‘highly unlikely’ actually represent? Do we know what these would need to mean for Julia, for her to accept the technology? This leads us to another question: Does anyone really understand all the potential benefits and risks of any one piece of technology? The clinicians who are hoping for reduced morbidity, likely have limited understanding of the technology. For instance, while a clinician may recommend a fall detection system for the home, it is unlikely they would be able to answer technological questions from their patients, such as “Which is better, a Bluetooth or Wi-Fi sensor system?” Similarly, if the patient were to ask the system’s designer, who knows the coding of the algorithms and the sensor technology, “What are the risks if I fall and the system does not detect my fall?”, the system designer would be unlikely to provide a clinical answer. Since no one person has all the requisite information, who should obtain the informed consent from the older adult, and who is more likely to be ‘honest?’ It’s impossible to know everything, and it’s impossible to get consent on each and every element within a supportive smart home system, therefore, the information you obtain regarding each element of the system, will depend on the individual you are speaking with. These issues then lead us to the questions of: “How does one adapt the technical information about risks and benefits into language that older adults can understand—allowing informed consent without showing any bias?”, “How does a system designer explain the algorithms used to protect and anonymize the data—in language aging adults can understand?” and “How does a clinician explain the causes of falls, and their consequences, using practical language?”. Trade-Off In the end, every decision we make has trade-offs. In the simplest terms, if we spend $100 on a pair of shoes, that money cannot go towards groceries, housing, or other clothing. When we consider ethical principles, sometimes two principles may be pulling in opposite directions, for instance, beneficence may suggest a certain technology will be helpful, but autonomy may suggest the person would want to live without technology in their home (Fig. 12.3). Other times, one principle may pull in opposite directions, for instance, weighing them against short-term versus long-term benefits. In Julia’s case, her short-term concerns for autonomy and self-determination may favor “I will decide myself how many beers I am going to drink tonight.” On the other hand, after 5 beers, she may fall down the stairs and have a significant injury or even death—which would have a very significant impact on her functional autonomy in the long-term. Considering these, Julia may then be willing to trade her short-term decisional autonomy; to increase her chances of keeping her long-term functional autonomy. Since Julia has some mild cognitive decline, how able is she to make a decision on the trade-off between autonomy (immediate decisions about beer drinking today) and beneficence (less injury in the medium term).

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Fig. 12.3 Decision-making. Potential tradeoffs to consider. Image on Freepik

12.4.2 Ethical Issues Related to Technology Up to now, we have considered ethical principles as they may pertain to making decisions about the technology’s implementation. This section will examine some ethical issues that need to be considered regarding technology. Access Having access to technology goes beyond having the financial means to purchase the required elements. Fang (2022) has identified a number of factors that can affect access. For instance, one of the predictors of technology use is exposure—especially through education or employment. Age and generation are also important and overlap a bit with the exposure, as the oldest old (over 85 years old in 2020) would not have used computers at work and their peers would be less likely to use technology as well. Furthermore, cognitive and physical limitations can prevent the use of some technologies. For example, arthritis can impact typing on a smartphone, or persons living with cognitive decline can have difficulties learning how to use new technologies. Additionally, geographic location can impact the ability to obtain the high-speed internet required for supportive smart home technologies. Lastly, the presence of children and/or grandchildren in someone’s life is also a significant factor because they are often involved in the teaching and installation of technologies for older adults (Fang 2022).

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Technology There are aspects in the technology design itself that creates ethical challenges for older adults. The first is because many scientists and technology designers consider aging as a “problem that needs to be fixed” (Boger 2022). This mechanistic view of aging affects the design and usefulness of the technology. Similarly, technology has a history of being disruptive and unpredictable (Sixsmith 2022), two qualities most older adults are not likely to favor. Hence, there is often a mismatch between the design of the technology and how older adults think and operate. Technology’s design is often impacted by multiple stakeholders, and much too frequently, the older adult is not considered as a stakeholder at all. Technology companies are driven by profit, which is not likely to be helpful to older adult users. Clinicians’ concerns about the health of older adults may result in designs of technology invading older adults’ privacy. Finally, the healthcare/wellness payor will be looking to use technologies to keep the older adult ‘out of hospital’ or to ‘save on staff time’ which may not necessarily line up with older adults’ wishes (Sixsmith 2022). Data and Analytics When humans take shortcuts in thinking, it can often lead to bias and even prejudice. In so doing, ‘age scripts’ (Rubeis 2022) develop, these can perpetuate stereotypes of older adults being simply the sum of their accumulated deficits. In the scope of technology, this is also referred to as ‘digital ageism’ (Chu et al. 2022). To demonstrate how the effects of technological bias and prejudice affect AgeTech, we will discuss machine learning. The purpose for machine learning is to help comb through large amounts of data and find patterns, essentially, to find shortcuts to aid us in making determinations. However, the way big data is collected can perpetuate the digital ageism problem. For instance, how is it possible for an AI system to properly classify an 82-year-old of African descent when the dataset this AI was designed upon, and used to make determinations, only contains thousands of 35- to 50-year-old Caucasians? How would it deal with people of different sexual orientations, religious/cultural backgrounds, and access to digital opportunities (Sixsmith 2022)? This leads to a catch-22, where big data is trained to identify patterns using biased, non-inclusive data sets at a time when AgeTech users hope for individualized, personal care (Rubeis 2022). Algorithms are the tools that comb through big data. Who are the programmers writing the code for these algorithms: typically, males between the ages of 20 and 40, and this can have a knock-on effect on how the data gets sorted. For instance, commonly used big data sets of facial images were found to divide age groups as follows: young adults grouped as 13–19 and 20–36, while all older adults were grouped in 66+ (Chu et al. 2022). Most of us would agree that faces of 60-year-olds look different from those of 80-year-olds. Similarly, if a database is trained to diagnose an illness in a population of 50-year-olds, how capable will an AI trained on that dataset be able to diagnose the same illness in an 80-year-old? Supportive smart systems are meant to learn patterns and then create an action to improve the situation. So, in a case where facial recognition software collects emotion

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data through facial expression, and for this example ‘mood,’ if every time the older adult frowns, might the system then initiate the playing of some ‘mood’ music? But, if the older adult finds the canned music annoying, they may learn not to frown when in the presence of the camera, to avoid having to listen to it. Does this actually mean they are happier? Similarly, a system designed to notify an informal caregiver when the older adult has not had breakfast by a certain time, might lead the older adult, after a night of poor sleep, to get up earlier than they would like, simply to avoid the caregiver’s phone call. In this case, by getting up before they have properly rested, might they be increasing their risk of falling? The impact of virtual assistants Smart home systems, such as Google Home and Amazon Alexa, have a component called virtual assistants. Typically, these can answer questions by scanning for information on the internet (e.g., Alexa, what is the capital of Mauritius?). Newer versions are designed to evaluate performance of some activities and even help ‘push’ the older adult to improve. Sometimes this can lead to the ‘autonomy-safety’ paradox. As an older adult becomes more autonomous, the virtual assistant may keep pushing them until the new (more challenging) activity actually becomes dangerous. For instance, if the virtual assistant’s system has a canned program for improving mobility within the home, it may encourage the older adult to go up the stairs as part of their ‘endurance exercise,’ even though their heart condition is not stable enough to support climbing stairs. In addition, the support provided by virtual assistants, be they emotional or physical, may actually lead to a reduction in seeking social support from humans. In contrast to virtual assistants being used to increase exercise, we also know that virtual assistants can also lead to laziness and a decrease in physical activity. For example, asking Google to ‘turn on the lights’ means no longer getting up to turn on a light switch and therefore losing the mobility and physical activity this act normally supports (Sixsmith 2022). System control When older adults are asked about the potential use of supportive smart home solutions, one of the most important themes for them is ‘control’ (Berridge et al. 2022) (Fig. 12.4). The older adults want to be able to control the system in multiple ways. They may want to be able to ‘pause’ individual sensors (e.g., bed sensor that could determine how many persons are in the bed), and especially be able to control video cameras, be they stationary (attached to a wall/furniture) or attached to a device (tablet or social robot). Older adults also want to be reminded about the data that is being collected and what information these data are able to provide. They would like to be able to test out the system before making a decision and also maintain the ability to regularly re-assess how they use it. Allowing for these types of control mechanisms will significantly increase older adult acceptance and retention of supportive smart home technology (Berridge et al. 2022).

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Fig. 12.4 System control. Who will dictate what the technology will do? Image on Freepik

Potential solutions The most important method to combating AI ageism is to create transdisciplinary (Choi and Pak 2006) teams using participatory design (Rubeis 2022). To design great AgeTech, the transdisciplinary team should include technology designers, human factors experts, older adults, informal caregivers, formal caregivers, and healthcare professionals. And each of these team members should have equal input in developing the goals of the system and its look and feel (participatory design). This will ensure greater variability in the goals for the technology and how the technology is assessed. Having a diverse team will increase the probability that the training data will be more diverse (Chu et al. 2022); and that the algorithms are more appropriate for older adults. For this to become the new modus operandi will require leadership at multiple levels (Fang 2022): • Research funding agencies should require transdisciplinary teams for AgeTech research. • Practice guidelines need to be developed for programmers in the AgeTech space. • AgeTech policies need to be developed by local and national governments. • We need to teach future generations of the importance of ethical design in AgeTech. To summarize, there are assumptions that supportive smart home systems are designed to do good—to allow an older adult to age in place. However, sensor data have the potential to be misused. Ethical principles exist to help guide the approach to designing and implementing technology. While we want to allow the older adult full autonomy in decision-making, we recognize that this cannot always be the case (depends on the health of the person making the decision). Therefore, societies need to consider public policies which take into consideration ethics and more.

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Public Policy

Each society has a social and legal history, informing their current public policies. Here are concepts we believe different societies need to consider for supportive smart home technologies. Justice (social and procedural) The spirit of justice is about fairness, where people receive what they deserve. There are two components to this concept of fairness: social and procedural justice. Social justice refers to the distribution of resources, wherein all people in a society should have equal access to health, wealth, rights, privileges, and opportunities. Procedural justice refers to the intervention approaches, the processes which resolve disputes and allocate resources. When considering the realm of supportive smart home technology, policymakers need to consider: “Who has access to supportive smart home technology?” and “What will be the process by which they can obtain it?” Who pays? At this point in time, smart home technology is considered a commodity, like a smartphone or a laptop. In most societies, individuals have differing economic status—there will be households that can afford to have multiple smartphones and others that will not be able to afford any. If supportive smart homes become an adjunct to publicly paid home care services, to enable older adults to age in place, then will society have a role to play in the distribution of these technologies? More bluntly, would a country’s taxpayers or health insurers have an incentive to provide seniors with these supportive technologies to delay going into more costly institutional care? The approach in a personal or employer paid insurance environment will be similar, with the purchasers and insurers weighing costs and benefits. Meaning, will the cost of the installation and maintenance of the system create savings long-term, which in turn, increases the employers’ or insurers’ profitability? Automated data: data ownership and accountability Another challenge for supportive smart home technologies encompasses the ownership and the accountability for the data. Each house will generate billions of data points. Privacy laws, such as the General Data Protection Regulation (GDPR) in the European Union (EU), articulate that private data need to stay private. However, when data leave the home to be interpreted, who is responsible to ensure the data remain private? Is it the companies that make each individual sensor, the hub makers, the data transmission provider, the data storage facilities, the in-home installer, or the clinician(s) that recommended the installation to the older adult (to name a few)? Is no one responsible, or is everyone responsible if there is a privacy breach? When the data have been converted to information, who is responsible that the expected activity follows? At the end of the day, when you aren’t able to get out of bed and no one comes to help, who is accountable for the consequences?

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Public Policy

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Wellness data versus health data There is a difference between wellness data and health data. Wellness data might be the number of steps a person takes in a day, which a smart device can roughly measure. Whereas health data, such as hemoglobin levels, which assess levels of protein in red blood cells, currently cannot be performed outside a lab by a medical professional. In most western societies, health data are usually generated from tests ordered by a physician, and these results return to the ordering physician—hence why it is the responsibility of the physician to guard this information (Fig. 12.5). While most western societies rightly ascribe to the principle that health data belongs to the individual, there is an understanding that health care professionals generate the data (e.g., request a CT scan of a patient). The facility that provides the test and the professional that receives the results, both have the duty to maintain the data securely (secure the CT scan in the patient’s file in the radiology department and in the professional’s office). When a publicly funded supportive smart home generates wellness data, who then would be considered responsible for the accuracy and interpretation of the data? As we mentioned in an earlier chapter, each household has the potential to generate billions of data points per week. With this volume, who will guarantee the accuracy of the data and its interpretation? If the algorithms lead to the wrong interpretation of the data, and an older adult directly or indirectly suffers (e.g., ambulance not summoned), who is accountable? Is it reasonable to expect the older adult to verify the data being generated in their home? Alternatively, should the home care organization (e.g., Carefor Health & Community Services in Ontario, Canada) that distributes the technology, be responsible for the data? Data ownership and accountability is absolutely clear for health data (legally ascribed), but ownership and accountability are completely unknown for wellness data. Additionally, there is already considerable (too much?) health data generated throughout our lives. Most jurisdictions struggle to bring existing data together for health care providers. For instance, to avoid duplication of tests, if the emergency room physician does not have timely access to chest X-Ray results taken the day before at another hospital, they might have to redo the test today. Furthermore, there are indirect healthcare data that are currently generated (e.g., length of stay in hospital, cost of hospitalization, discharge location), which could be used by the healthcare system to improve patient care outcomes. There are also organizations, like ICES in Ontario, Canada, which bring together direct and indirect data for the purposes of research and quality improvement. Would supportive smart home data be shared with these types of organizations? As supportive smart home technologies provide increasingly precise and organized information on physical and cognitive well-being, would it not be useful to have it available, for instance, in an emergency room? If, for example, an older adult has difficulty explaining which medication they take and when they take it, it would be helpful for the ER physician to know if this is a new issue (possibly associated with a delirium) or if it is a longer standing ‘normal’ for them. This knowledge could be made available in an easy to find and use ‘medication use module’ derived from a supportive smart home.

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Fig. 12.5 Health data. Image by Freepik

Box 12.1 ICES

ICES is an independent, non-profit corporation that applies the study of health informatics for health services research and population-wide health outcomes research in Ontario, Canada, using data collected through the routine administration of Ontario’s system of publicly funded health care. https://www.ices.on.ca/ Wellness devices versus medical devices There are distinct, yet important, differences in devices that are certified medical devices, and those which are labeled as wellness devices. For instance, blood pressure monitoring devices must adhere to a ‘medical device’ standard, which requires extensive testing and licensing by regulators, such as Health Canada (or the equivalent agency in other jurisdictions) in order to be marketed to healthcare facilities and consumers. There are international standards on the precision of such devices. On the other hand, a smartwatch may have a blood pressure function related to wellness, but it does not carry a regulator’s seal of approval to be considered a medical device. And while smartwatch manufacturers have a commercial interest in the device having a semblance of precise measurements, presumably the absolute values for blood pressure are not as vital. For obvious reasons, a medical device may cost much more than a wellness device, depending on its application.

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We must then ask, are supportive smart home devices wellness or medical devices? If components in a home are considered medical devices, is the taxpayer, in countries where healthcare is a public good, willing to pay for the required markup of the price? How will a higher cost affect the distribution of the devices (social justice)? And who is accountable for these medical devices? Redundancy As you may recall, in Chap. 5, we mentioned that there was a critical infrastructure failure that led to millions of Canadians not having internet or cellular phone service for over 24 h. Most systems that are ‘critical’ have some type of backup redundancy built in. At some point, batteries of in-home devices will drain, requiring a replacement. To mitigate for a hub going off-line because of a power failure, back-up batteries could be installed so that sensor data can continue to be collected via Wi-Fi. To mitigate for a potential transmission failure, data transmission backup is possible if the system can temporarily store data at the home hub, until connectivity returns. In turn, many data centers have back-up generators to ensure data is not lost. Some data centers also have alternate back-up sites, in case the physical infrastructure is affected in one location. Ideally, there would be multiple layers of redundancy to ensure supportive smart home systems remain functional. Standards Public policy should also support the development and application of standards for supportive smart home technologies. This is important so that the various devices made by different companies are able to interconnect and ensure newly built homes come predesigned with supportive smart technologies in mind. In 2019, a number of companies in the smart home space, including Amazon, Apple, Comcast, Google and the Zigbee Alliance, created a working group to develop specifications for the ‘Connected Home over IP’ project. Version 1.0 of the specifications to standardize the hub components of smart homes was published 4 October 2022 and named ‘Matter.’ In addition to hub standards, there is the possibility of further standardization, both upstream and downstream of the hubs. Upstream, there is room for sensor-level communication protocols and standardization of battery power and wireless power sources. Downstream from the hubs, there is the potential to develop security protocol standards and maybe even data standards for cloud-based activities.

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Summary

Supportive smart home technology is new, so we are still trying to understand the ethics and public policy implications of it. We need to think about ‘how we think about the ethics’ that will inform policy. In fact, some authors are advocating for ‘ethical adoption’—a deeper integration of ethical principles into the design, development, deployment

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and usage of technology (Robillard et al. 2018). Each jurisdiction will be applying ethical principles to their unique needs. However, because people are going out to buy these systems today, at a time where there is very little public policy in this space, are we leaving it to private industries to create the standards? A case in point, the private market approach to handheld devices was great in getting development to advance quickly, yet manufactures of these handheld devices (cellular phones, tablets, earbuds, digital cameras, headphones, etc.) chose to use varying power adapters in their product design. This meant consumers were often required to purchase new adapters each time they purchased a new device; and resulted in copious consumer waste when adapters were no longer needed. The EU has passed legislation, whereby, “consumers will no longer need a different charging device and cable every time they purchase a new device, and can use one single charger for all of their small and medium-sized portable electronic devices…[where products] rechargeable via a wired cable will have to be equipped with a USB Type-C port, regardless of their manufacturer” (Yakimova 2022). Similarly, the Canadian Government included a directive regarding universal charging devices in their 2023 Budget (Government of Canada 2023). Might it be advantageous for policymakers to work with manufactures to streamline these standards earlier on? While we have discussed ethics and policy in the chapter, there exists just as much work to be done within legal frameworks. Each jurisdiction will need to shore up their legislative processes with regards to supportive smart home technologies.

12.6.1 Key Initiatives APTTA is an AGE-WELL National Innovation Hub dedicated to advancement of policies and practices in technology and aging. They are located in Fredericton, New Brunswick, Canada and are studying the policy challenges to moving AgeTech from the research lab into people’s homes (https://agewell-nih-appta.ca/).

12.6.2 Find Out More ICES is an organization that collects healthcare data for residents of Ontario, Canada: https://www.ices.on.ca/. https://agewell-nih-appta.ca/. McMaster Optimal Aging Portal is a free online portal consisting of evidence-based Blog Posts, Web Resource Ratings and Evidence Summaries, providing a “trustworthy source of information about health and social aspects of aging”: (https://www.mcmastero ptimalaging.org/). For a more detailed discussion on autonomy and independence, the reader is also referred to the second book in the AGE-WELL series, Autonomy and Independence: Aging

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in an Era of Technology, by Liu et al. https://link.springer.com/book/10.1007/978-3-03103764-1.

References Berridge C et al (2022) Control matters in elder care technology: evidence and direction for designing it in. In: DIS. Designing interactive systems (conference) 2022, pp 1831–1848. https://doi. org/10.1145/3532106.3533471 Boger J (2022) Culture change, human-centered design, and ethical by design as transactional cornerstone concepts in the development of technology for supporting aging. In: Proceedings of the 15th international conference on pervasive technologies related to assistive environments. Association for Computing Machinery (PETRA ’22), pp 556–561. https://doi.org/10.1145/3529190. 3535692 Choi BC, Pak AW (2006) Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness. Clin Invest Med 29(6):351–364. PMID: 17330451 Chu C et al (2022) Examining the technology-mediated cycles of injustice that contribute to digital ageism: advancing the conceptualization of digital ageism: evidence and implications. In: Proceedings of the 15th international conference on pervasive technologies related to assistive environments. Association for Computing Machinery (PETRA ’22), pp 545–551. https://doi.org/ 10.1145/3529190.3534765 Fang M (2022) Future of AgeTech: transdisciplinary considerations for equity, intersectionality, sustainability, and social justice. In: Proceedings of the 15th international conference on pervasive technologies related to assistive environments. Association for Computing Machinery (PETRA ’22), pp 536–541. https://doi.org/10.1145/3529190.3534757 Government of Canada (2023) Budget 2023. Government of Canada, Department of Finance, p 37. https://www.budget.canada.ca/2023/home-accueil-en.html#downloads. Accessed 3 Apr 2023 Robillard JM et al (2018) Ethical adoption: a new imperative in the development of technology for dementia. Alzheimer’s & Dementia: J Alzheimer’s Assoc 14(9):1104–1113. https://doi.org/10. 1016/j.jalz.2018.04.012 Rubeis G (2022) Complexity management as an ethical challenge for AI-based age tech. In: Proceedings of the 15th international conference on pervasive technologies related to assistive environments. Association for Computing Machinery (PETRA ’22), pp 542–544. https://doi.org/10. 1145/3529190.3534752 Schicktanz S, Schweda M (2021) Aging 4.0? Rethinking the ethical framing of technology-assisted eldercare. Hist Philos Life Sci 43(3):93. https://doi.org/10.1007/s40656-021-00447-x Sixsmith A (2022) Ethical challenges in aging and technology. In: Proceedings of the 15th international conference on pervasive technologies related to assistive environments. Association for Computing Machinery (PETRA ’22), pp 552–555. https://doi.org/10.1145/3529190.3534756 Wallace B, Knoefel F (2022) Ethics | Law | Policy and the Supportive Smart Home (Evidence to Impact: A Research Partner Series). AGE-WELL National Innovation Hub - APPTA. https://age well-nih-appta.ca/dr-bruce-wallace-dr-frank-knoefel/ Yakimova Y (2022) Deal on common charger: reducing hassle for consumers and curbing ewaste|News|European Parliament. News European Parliament, 6 July 2022. https://www.eur oparl.europa.eu/news/en/press-room/20220603IPR32196/deal-on-common-charger-reducinghassle-for-consumers-and-curbing-e-waste. Accessed 27 Mar 2023

Reflections

13.1

13

Our Challenge

You’ve just finished reading this book and you’ve probably got lots of ideas, and perhaps just as many questions—and these may be different depending on if you are an older adult, informal or formal caregiver, student, engineer/computer scientist, healthcare professional, working in a business, or someone developing health policy. What this book did not do This book is not a complete compendium of information—the “aging in place/supportive smart homes” field is advancing so quickly that during the time you’ve read the book, new technologies for supportive smart housing have been researched, developed, and commercialized. As a primer, this book does not attempt to provide you with heavy technical information, but most chapters have a ‘read more’ section that points to additional resources. And finally, this book does not set out to provide you with information that can help with your own assessment or diagnostic needs. Rather, you will hopefully be able to participate in an informed discussion with your healthcare provider or family regarding if and how you might be able to incorporate sensors into your home—so you can age in place. And although technology can assist us in aging in place, we cannot, nor should not eliminate, or ‘farm out,’ human-centered supports. There can be no replacement to the quality or variety of health and well-being supports and relationships that other humans can provide, especially when those others are loved ones. So, what does this book provide? This book is a primer and is intended to give you a high-level overview of the concept of supportive smart housing. It is understood that this covers rapidly changing technology and that the text is current at the time of publication.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 F. Knoefel et al., Supportive Smart Homes, Synthesis Lectures on Technology and Health, https://doi.org/10.1007/978-3-031-37337-4_13

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You’ve read the history of how we’ve advanced from homes that provide shelter—to homes that record and provide data to someone somewhere—to smart homes that can use data to create useful knowledge—to the emerging iterations of the supportive smart home where the house captures data, creates knowledge, and builds the intelligent response to support someone living in the home. We hope that we made it clear from the first page that older adults want to age in place, and we hope that what you have read makes a case for the importance of the supportive smart home, wherever home is for the individual. Although this book targets the older adult, their families, and their care team, there is increasing interest in the ubiquitous implementation of supportive smart homes that can support accessibility and universality for all. You’ve read about the myriad of sensors that can be used to collect data from various parts of the home. Water, heat, motion, door, and bed sensors can be used individually or together. You’ve learned about how data from these sensors can be converted into information about the home occupants. You have read about technology ranging from simple sensors that function as alarms, to complex systems that require computational analytics fusing information from multiple sensors. When choosing which technology to use, cost, ease of use, and accessibility, are only a subset of the important considerations. Different uses for the data were presented, including the potential to create feedback or interventions to cue behavior(s) and opportunities to push data from the home to the healthcare provider or family! Each of these uses requires processing. So, when implementing a technology, it is important to understand where the processing can be done: locally and/or in a cloud-based data management system. Power requirements, Wi-Fi and internet stability, and cloud computing capacity should also influence the choice of appropriate sensor technologies. You’ve read a number of different use-case scenarios that highlight potential technologies to support aging in place. Even though we may face challenges as we age, we’d like to stay autonomous and independent (Fig. 13.1). Various off-the-shelf technologies can help us remain socially connected, physically active, and intellectually stimulated. Our ability to complete activities of daily living and manage day-to-day needs might change, leading to increased risk of falls, challenges with managing medications, and exploring at inopportune times. Our general health condition, mobility, and cognitive changes may create challenges for ourselves and our caregivers. Older adults often speak about not wanting to be a burden, so caregiver stress is an important consideration for aging in place and supportive smart home technology. Finally, you have had a chance to reflect on ethical principles and how public policy needs to consider supportive smart homes in our complex societies. Designing a sensor and app is not enough. Do the benefits of the sensor outweigh the risks? Who decides if this sensor should go into the older adult’s home? Who pays for the sensor system? Who decides where the data and information derived from the sensor system go? Who is responsible to ensure that the data and information are secure and privacy is not breached? How will society ensure that once supportive smart homes are part of our communities,

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Fig. 13.1 Aging well. Agnes Laurin appreciates living within her community. Used with permission

the systems are ‘on’ 99.9% of the time and minimize false positive and false negative alarms and notifications? These challenging questions present valuable opportunities to forge a future wherein we could safely age in place. A couple of additional thoughts: (i) consider that currently available sensors can be repurposed for new solutions (save time and money); (ii) work with the end user—use flexible, inclusive, user-centered approaches as these are most conducive to identifying the needs and effective solutions with and for older adults; and (iii) remember that developing supportive smart homes requires a transdisciplinary team.

13.2

Transdisciplinary Approach

Older Adults and Caregivers As an older adult and/or caregiver, you may have read this book in order to understand what different supportive smart home solutions are available. It’s likely that you had a specific scenario or need in mind—or hopefully reading about sensor capabilities and understanding different personas has helped you identify or clarify your own needs. If you are new to this area, we hope that we have been able to help you understand how problems/challenges associated with aging can be addressed with technology; and how to find out about potential solutions.

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Over the last number of years, there has been an explosion of interest in aging in place. The amount of technology currently available is overwhelming and this is leading to a challenge in identifying and finding the relevant systems for our needs. Finding the potential solution and identifying how to procure or access the technology can be tricky. In addition, some systems seem to require advanced degrees in engineering, design, or medicine to implement! Our ability to access resources and our levels of digital literacy are being challenged. Hopefully when you recognized one of the use- cases as relevant, you appreciated the Find Out More sections and the list of references. Another useful resource is the AGE-WELL website (see https://agewell-nce.ca), which refers to a number of AgeTech solutions. We also recommend you talk to your healthcare provider and visit local stores to learn more about supportive smart home technologies. We hope that the information provided will help you reach your objective of aging in place (Fig. 13.2).

Fig. 13.2 Bill and Sheila Thomas enjoying cottage living. Used with permission

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Engineers and Computer Scientists We hope readers with engineering and technical backgrounds have found value in the four clinical chapters (Chaps. 6–9), and in particular, have a better understanding of some of the challenges that supportive smart homes need to address. We hope that these have inspired you to find new technological solutions to support the challenges encountered by aging adults who hope to stay living at home for as long as possible. In addition, we hope that the chapter on ethics and policy has helped you reflect on the complexity of not only implementing technology but also the risk of biases in the designs. Please reach out to older adults and healthcare professionals to strengthen the solutions you are designing. Healthcare Professionals We hope that healthcare professionals have gained a better understanding of sensor technology and data analytics after reading the two technical chapters (Chaps. 4 and 5). Our hope is that we have inspired you to think about how technological solutions might help your aging patients in the near future. In addition, we hope that the chapter on ethics and policy has provided you with further reflections on the complexity of implementing technology. As you continue your practice, consider visiting your local smart home technology store to further your understanding of what is available. Please reach out to engineers and computer scientists if you have ideas of areas in clinical care where technology might have a solution. Entrepreneurs and Business Owners Different parts of this book will also be of use to entrepreneurs and industry. You’ve read about the significant increase in the size of the aging population and that a majority of older adults want to age in place (Fig. 13.3). The healthcare sector is providing home and community care—a critical need. But if it is possible to develop and offer solutions to tech-solvable challenges, this will support and allow caregivers to use more of their time to provide care. We hope our chapter on ethics and policy has guided your reflections on the complexity of implementing technology. The AgeTech sector is a great place for innovation, but please include older adults, their families, and healthcare professionals as you develop prototypes. Policy Makers Around the world, on average, people can now expect to live longer, healthier lives than in previous generations. This is good news for people! The Canadian National Framework on Aging identifies five key principles: dignity, independence, participation, fairness, and security. The design and integration of supportive smart technologies into homes to allow aging in place must reflect these principles. We hope we have provided information that government policy planners, decision-makers, and other stakeholders can use when designing policies and programs for supportive smart homes. Please reach out to older adults, clinicians, and engineers as you develop much-needed policy in this area.

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Fig. 13.3 Elderly couple who may benefit from living in a supportive smart home. Image by wirestock on Freepik

For students Depending on your discipline, we hope that you will have gained an understanding of how technology will have an important role in helping older adults age in place. We hope the chapter on ethics and policy has helped further your reflections on the complexity of implementing technology. Maybe by reading this book, you have identified gaps that you are interested in filling with a new generation of solutions. Please work with older adults to improve the availability and access to supportive smart homes in the future. Thank you for having taken the time to read this book. There are many real opportunities for you to make a difference in this space. Some of you may have personal reasons for your interest—perhaps a grandparent or neighbor who could be better able to live independently if the technologies were available and well-integrated into the home. Some of you may be in a college or university program and have an interest in a career path in research, government, not-for-profit, or private sector. We hope you will be part of the new generation of highly qualified leaders who are accelerating the delivery of digital health solutions for older adults with complex health needs and their caregivers to support aging in place (Fig. 13.4). The field is complicated, and the skill sets you will develop as you create new solutions or implement aging in place solutions to create supportive smart homes, are currently not typically taught in academic programs. However, there are excellent learning programs, like the EPIC program offered by AGE-WELL (see https://agewell-epic.ca), that are worth completing. As you increase your skills, reflect back on the different aspects of this book. It’s clear from the use-cases that the starting point is to identify the problem—and no,

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Fig. 13.4 2019 4th AGE-WELL Summer Institute. Students from across Canada, the United States, and the United Kingdom took part in the 4th AGE-WELL Summer Institute, held in July 2019 in Montebello, QC, Canada. They learned from researchers, entrepreneurs, and people with lived experiences. Courtesy of AGE-WELL

you can’t do this in isolation (or at least that’s not the best approach). Define the issue/ challenge for (and with!) the older adult and/or end user, keeping in mind that these may not be the same person. Work with the end user to find or develop the solution. Co-design/ co-development is the best. The authors of this book include physicians, engineers, scientists—with broad experiences in signal analysis and processing, health, aging, social science, human factors, and other relevant disciplines. Their research teams include older adults, families, and caregivers—and of course—students and trainees! Look for teams working in this area and reach out—identifying solutions for complex problems needs lots of different ideas. To All Readers Most importantly, we hope that we have been able to show that everyone has a role in the development of complex, ethically sensitive technological solutions to facilitate aging in place. Only transdisciplinary teams, that contain members of all the above-mentioned groups, and likely many others, will be able to successfully tackle the complexity of this area. While the rest of the book has been a compilation of the various authors’ experiences, one of us has personal experience dealing with home technology trying to support aging in place. Here is their story.

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One Last Case—Closer to Home

2020—COVID! We’re on our way out for an appointment but Dad is complaining of numbness in his arm and leg weakness. A quick call to his doctor and he’s on his way to the hospital. Full workup and he’s discharged with a follow-up call with a neurologist planned for a couple of days later. The appointment is a telehealth video call—easier than going in person—but neither Mum nor Dad are tech users at this point so I’m there for the appointment. Dad has difficulty hearing, so video conferencing is tough. The neurologist thinks Dad forgot to take his Rivaroxaban (Xarelto)—he’s managing 6–7 pills with a pill box that he fills each week. He’s adamant that he takes his meds as prescribed. Our solution—Mum was able to get a used tablet computer and has really learned how to use it well. This not only helps for telehealth, but she can now read the paper, listen to music, and most importantly, use the communication tool to video conference with children, grandchildren, and now great grandchildren. Dad’s ok with the tablet, but when there are more than two people on a call, he’s not sure where to look and the sounds get muddled. He’s also not sure where the people physically are, so that’s confusing. To help manage Dad’s meds, his pharmacy now provides him with weekly blister packs. 2021—Brrring—05:30. Phone rings and when I answer, it’s Mum in a panic. Dad’s left the condo. He got up and is so quiet that by the time she registered that the front door closed, he was on his way to the elevator. She didn’t make it to the hall before he was already gone. It’s a good thing I’m only a 13-min drive away—and that it’s March and not January. At least that’s what I’m thinking as I throw on my winter coat on the way out the door. I drive around their building to see if I can see Dad in the field or walking along one of the paths—no luck. I park, walk into the building, and run into the superintendent coming out of his unit—he had just had a call from someone on seven who let him know that there was an older gentleman at her door. I take the elevator up and there is Dad in his pjs and slippers. He knew he was in his building but couldn’t remember what floor and unit he had to go back to. They live on the third floor. When I ask why he left the unit so early, he can’t give an answer. He just left. Mum was waiting at the doorway—relieved but also confused. She kept saying “He’s so quiet. I just didn’t hear him. I always wake up when he gets out of bed, but I was just too tired.” It’s clear we need to do something to keep this from happening again—keep Dad safe and help Mum sleep and not feel guilty. Our solution—We installed a door alarm. This works well for Mum at night and even during the day—it goes off when the door opens. Adding verbal cues with a smart hub to help Dad get back to bed hasn’t worked—he can’t hear so when he gets the cue, he starts asking the speaker to speak up. Since the intent is to let Mum sleep, turning up the sound didn’t work very well. We’ve also added motion activated lights to help guide Dad to the bathroom, which is where he is normally going during the night. The lights have also made it easier for Dad to see his walker and other supports, so he is less at risk for a

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One Last Case—Closer to Home

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fall. We’ve had some challenges with the blister pack since it’s now challenging for Dad to track the day of week and the time of day. 2022—Splash! 02:00. The floor is flooded outside the bathroom—the water is overflowing; Dad is sitting on a bench beside the sink but not moving to turn off the water. Our solution—We installed flood detectors. The home has two bathrooms, so we placed a detector on the floor at the base of the shower, bath, and toilet, as well as on the floor below the bathroom and kitchen sinks. Since the alarms were installed, one went off. This was the middle of the day and water was running on the floor during a shower. Dad has always been an amazing ‘fixer,’ so the alarms are often on the counter with the battery removed. Next steps may be smart taps or a water monitor to set off an alarm if water is left running for a longer than usual period. Challenges we still have We’re still having challenges with the blister pack, although Mum now puts his meds into a small cup at each meal. We’ve realized that if something happened to Mum, if there was a fire or other emergency, Dad wouldn’t necessarily be able to call 911. We tried a ‘photo’ phone, but the two-step process was sometimes too complicated—and in an emergency, the system has to work! Dad sometimes gets up in the middle of the night and ends up falling asleep on the couch in the living room or office. This isn’t a problem for him, but it adds more stress to Mum, who wakes up to find him gone from the bedroom. Motion sensors would help identify where he is, or at least where he was last, but it’s not clear how to make this data usable. Over the last couple of years, both Mum and Dad have had to record their blood pressure and heart rate for short periods. They write the numbers on a pad of paper and then call the doctor’s office weekly to report the numbers so the doctor can verify that they are ok. Wouldn’t it be nice if these devices were seamlessly integrated into the supportive smart home solution as well? As virtual care becomes more widespread, it would seem beneficial to integrate Bluetooth sensors with hospital or physician office systems. In addition, not all healthcare providers and patients have the digital literacy levels needed to use the technology or the information. Patients and providers have to develop a better understanding of how these technologies can be used effectively. What you may have realized with this scenario: What’s next? Call to action This is a 3-year snapshot of some of the challenges we’ve had as Mum and Dad age in place. They have a wonderful home and have lived incredible lives. They want to stay in their home, and we are doing all we can to make this happen. But over the last three years we’ve installed door alarms, flood alarms, communication tablets, smartphones, motionactivated lights—and we may still benefit from a medication dispenser, location sensors,

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remote care monitoring. The sensors and equipment we’ve installed are all siloed—they don’t work together. We’ve read a number of installation guides and have learned a little about technology—but we still feel illiterate. There are several groups working to develop supportive smart homes. As an end user, I could really benefit from interoperable systems—so we don’t have to buy and install 10 or 15 separate pieces. One system—kind of like a plug & play system—that allows me to add the sensor or equipment I need when I need it. One system with a support line so I can call for help and that will share my data with the people or organizations I want it to go to.

13.4

Summary

Now that you have a good understanding of what sensors can do and how they can be used to create supportive smart homes—and what age-related challenges can be addressed or mitigated with different technologies, you have the tools to make a difference in the lives of older adults and caregivers. Use this information to speak with your healthcare provider and help them identify specific challenges to address. Work with end users, older adults, and their caregivers to identify their challenges so the development of new systems targets real needs. Work with developers and engineers as they create new solutions. And most importantly, stay engaged with aging in place.

Glossary

Accelerometer-based sensors An accelerometer is a sensor that measures changes in motion. For instance, they are incorporated into devices that measure step count, such as smartwatches, and can measure vibration if attached to a hard surface. Activity of daily living (ADL) Is an activity people participate in on a regular basis. There are three types: basic (bADL), instrumental (iADL), and enhanced (eADL) (Please also see definitions for individual types within this glossary). AgeTech Abbreviation for ‘age technology’, which can include mechanical devices like canes and walkers, and computer technology used to help older adults. Aging in place Refers to having the health and social supports and services you need to live safely and independently in your home or your community for as long as you wish and are able (https://www.canada.ca/en/employment-social-development/corporate/ seniors/forum/aging.html). App An “abbreviation for application: a computer program that is designed for a particular purpose.” https://dictionary.cambridge.org/dictionary/english/app. Artificial Intelligence (AI) “The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” (https://www. oxfordreference.com/display/10.1093/oi/authority.20110803095426960). Autonomy The ability to make decisions for oneself. Basic activity of daily living (bADL) A basic activity of daily living pertains to the personal care of our bodies. Examples of bADLs include bathing, showering and personal hygiene, dressing, going to the toilet, getting in/out of bed and on/off a chair, and self-feeding. Big data Are extremely large data sets, requiring connectivity to the Internet, so they can be stored and analyzed computationally to reveal patterns, trends, or associations. Blister pack Is any of several types of free-formed plastic packaging used for pharmaceuticals. Typically, this allows a pharmacy to group together a number of medications that need to be taken at the same time.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 F. Knoefel et al., Supportive Smart Homes, Synthesis Lectures on Technology and Health, https://doi.org/10.1007/978-3-031-37337-4

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Chronic obstructive lung disease Chronic obstructive lung disease, also known as chronic obstructive pulmonary disease (COPD), is a progressive lung disease that causes a reduction in air flow. This affects the amount of oxygen the body gets and therefore affects well-being. Cloud computing Is the on-demand availability of very significant computer system resources for data storage and data analysis. These computer systems may be located in a variety of geographical locations and are inter-connected by the Internet. Cognition Refers to an individual’s mental abilities including memory, attention, language, decision-making, performing tasks, and spatial awareness. As we age, some of our cognitive abilities may decline over time, but with normal aging these declines do not typically significantly impact our ability to perform day to day tasks. Cognitive impairment Can include impairments in memory, attention, language, and decision-making, performing tasks, and spatial awareness. It can lead to reduced independence, where individuals are no longer able to perform daily functions. In most individuals, these cognitive impairments increase gradually over time. Color video camera A camera that uses visible light (red, blue, and green wavelengths: 400–700 nm) and produces images that we commonly see in photos and videos. Congestive heart failure Is a chronic disease of the heart, where the pump function declines, reducing the flow of blood to some body parts. This can lead to less oxygenation of the blood and hence decreases in the amount of oxygen vital organs will get. Two of the important symptoms associated with heart failure are shortness of breath (often associated with fluid overload in the lungs) and swelling in the legs (related to fluid overload in the legs). Data Within the context of a supportive smart home model, data are numbers/ measurements that come from a sensor, which typically have little meaning by themselves. Data analysis (or analytics) Is the process of taking data and converting it to useful information. Data fusion Using data from a number of sensors to improve the accuracy of the information. For instance, one motion sensor in a hallway can determine if there is movement in that hallway. But if there is no motion there, it is not possible to know where the occupant is. However, if there are multiple motion sensors, the system is more likely to be able to locate the person. Dementia An umbrella term for a condition where there is significant cognitive impairment, typically in multiple domains, sufficient to cause the affected person to be unable to perform daily tasks and activities that they were previously able to complete independently. Dementia has many different causes, most of them being neurodegenerative diseases, for example Alzheimer’s disease. Electronic nose A type of sensor designed to detect odors using chemical detectors. Enhanced activity of daily living (eADL) Is a higher order daily activity that leads to “fulfillment, well-being, quality of life, happiness, or social engagement” (Rogers et al.

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2020, p. 128, https://doi.org/10.4017/gt.2020.19.2.005.00) that is unique to the individual’s personality and autonomy. Examples include listening to music, going to concerts, enjoying sports, enjoying visual arts, and learning about history. False negative A false negative error is a test result that wrongly indicates that a condition is not happening. For instance, if a person rolls off a bed sensor but is still in the bed, the system may indicate that they are no longer in bed. False positive A false positive error is a result that indicates a given condition exists when it does not. For instance, the system indicates that a person is in bed when in fact they are sitting on the sofa watching TV and it is their dog that is lying on the bed. Formal caregivers Personal support workers (PSWs) and other unlicensed paid caregivers (not members of a professional college) who provide care in a person’s home. Health data In the context of a supportive smart home, this book defines ‘health’ data as data measured by a medical device, such as blood pressure or blood glucose level. This is in contrast to ‘wellness’ data. Healthcare professionals Are physicians, occupational therapists, physiotherapists, registered nurses, licensed or registered practical nurses (LPNs/RPNs), and any other members of a health profession with a professional college. Informal caregivers Family members, neighbors, friends, volunteers, and anyone else who is not paid to provide care. Information Within the context of a supportive smart home model, information is created when the sensor data are organized in a way that has meaning to a particular individual. Information and communication technology (ICT) Consists of a “diverse set of technological tools and resources used to transmit, store, create, share or exchange information” (UIS, 2009, https://learningportal.iiep.unesco.org/en/glossary/information-andcommunicationtechnologies-ict). They include devices (e.g., smartphones, computers, and tablets) and applications (e.g., email, the web, instant messaging, social media), which provide access to information and enable electronic forms of communication. Informed consent Is making an informed and uncoerced choice, based on the ability to understand the important elements of the decision. Instrumental activities of daily living (iADL) More complex daily living activities and tasks related to how we function in our environment, requiring higher levels of planning and thinking (e.g., managing our finances, cleaning our home, cooking our own meals, managing our medications). Knowledge Within the context of a supportive smart home model, knowledge is sensor information that has been processed, analyzed, and interpreted, and can then be used to perform an action. Machine learning “A branch of artificial intelligence concerned with the construction of programs that learn from experience”. 333 (https://www.oxfordreference.com/display/ 10.1093/acref/9780199234004.001.0001/acref-9780199234004-e-3056).

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Medical devices A ‘medical device’ generates health data. The device requires extensive testing and typically licensing by regulators. There are typically international standards on the precision of such devices, for instance, to measure blood pressure. Mild cognitive impairment (MCI) Refers to individuals who are found to have problems with memory and thinking on cognitive tests but remain independent in their day-to-day functioning, although these activities may take longer to complete (Peterson et al. 1999, https://doi.org/10.1001/archneur.56.3.303). Osteoblasts Cells that synthesize bone. Parkinson’s disease Is a progressive neurological disease of the brain. It is characterized by tremor, slowness of movement, rigidity, and postural instability. Over time walking becomes more stooped and the feet seem to stick to the floor, known as ‘freezing gait.’ Privacy Being able to control who has access to one’s personal information. Sampling rate The rate at which data are generated. For instance, video cameras generate 30 frames per second and a room thermometer may measure the temperature once every 5 min. Sensor A sensor is a device that produces an output signal for the purpose of sensing a physical phenomenon (Wikipedia). Sensors can detect such things as light, water, motion, or the status of a door. This book defines low-cost sensors as costing less than $100CDN, medium-cost between $100CDN and $499 CDN, and high-cost as those $500CDN and over (See Table 5.1 summary in Chap. 5). Sensor substitution Explores ways in which sensors can be used to measure things that they were not intentionally designed to directly measure, e.g., using a temperature sensor underneath the fridge door to determine if it is open. Serious game Serious games are games that have another purpose besides entertainment. They typically refer to computer-based games that may have a teaching or assessment goal. The game could be used to assess such dimensions as a person’s knowledge or reaction time, and the storyline may teach about topics as diverse as geography, history, mathematics, and languages (Wikipedia). Sleep apnea Is a condition where the patient has frequent extended episodes of ‘not breathing’ during sleep. This can decrease the amount of oxygen in the blood and affect the organs that rely on oxygen to function. In some cases, years of untreated sleep apnea can lead to brain suffering and cognitive decline (Gosselin et al. 2019, https://doi.org/ 10.1164/rccm.201801-0204PP). Smart home Is a home equipped with the ability to monitor and/or control the home environment (e.g., temperature, lighting, entertainment). It may include security elements and typically has a hub connected to the Internet that receives data and can send commands to connected devices. Social connectedness Defined as “the opposite of loneliness, a subjective evaluation of the extent to which one has meaningful, close, and constructive relationships with others (i.e., individuals, groups, and society)” (O’Rourke and Sidani, 2017, p. 43).

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Strain gauge Is a device used to measure strain on an object. It typically consists of an insulating flexible backing, which supports a metallic foil pattern, attached to an object. As the object is deformed, the foil is deformed, causing its electrical resistance to change (Wikipedia). Supportive smart home Is a home equipped with the ability to monitor the occupants and use that information to support their health and well-being. This is typically done using sensors, converting the data from these into information, and ultimately producing actions that support independent living. Thermal camera A camera that produces images using infrared radiation (1,000–14,000 nm wavelengths). The images produced show ‘colder’ areas in black/blue tones and ‘warmer’ areas in red/yellow tones. Transdisciplinary Usually used in a team setting, where people with very different backgrounds work together to solve a problem. In the process of doing so they take on experience from the other disciplines and this changes their practice in their discipline. “Interdisciplinarity analyzes, synthesizes and harmonizes links between disciplines into a coordinated and coherent whole. Transdisciplinarity integrates the natural, social and health sciences in a humanities context, and transcends their traditional boundaries” (Choi BC, Pak AW 2006). https://pubmed.ncbi.nlm.nih.gov/17330451/ Ultra-wideband (UWB) waves Ultra-wideband is a radio technology that can use a very low energy for short-range, high bandwidth communications (>500 MHz) over a large proportion of the radio spectrum (Wikipedia). Virtual assistant A computer program using artificial intelligence to respond to spoken or written commands, to perform tasks like playing music, answering questions, and executing commands. Synonyms include smart assistant, digital assistant, voiceenabled technologies, and voice-controlled personal assistant. Examples include Apple Siri, Amazon Alexa, and Google Assistant. Water flow meter A device that measures the amount of water flowing through a pipe, via a sensor placed inside the pipe. Wellness data Is data measured by non-medical devices (e.g., smartwatch) for items such as hours of sleep or number of steps taken. This is in contrast to ‘health’ data. Wellness devices A device (e.g., smartwatch) that measures ‘wellness data,’ for instance, daily step count, which does not carry a regulator’s seal of approval to be considered a ‘medical device.’ Wi-Fi Is the acronym for ‘wireless fidelity’ and refers to a trademarked wireless network protocol based on IEEE 802.11. It is the family of standards that allows devices to communicate with each other based on radio waves in the 2.4 and 5 GHz bands. Wisdom Within the context of a supportive smart home model, ‘wisdom’ is the synthesis of the sensor’s information and knowledge, along with the experience in understanding the patterns that occur, so as to apply a higher degree of knowledge and action.