Health Informatics and Medical Systems [1 ed.] 9781601323620

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PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON HEALTH INFORMATICS AND MEDICAL SYSTEMS

Editors Hamid R. Arabnia Leonidas Deligiannidis Associate Editors George Jandieri Ashu M. G. Solo, Fernando G. Tinetti

WORLDCOMP’16 July 25-28, 2016 Las Vegas Nevada, USA www.worldcomp.org ©

CSREA Press

This volume contains papers presented at The 2016 International Conference on Health Informatics and Medical Systems (HIMS'16). Their inclusion in this publication does not necessarily constitute endorsements by editors or by the publisher.

Copyright and Reprint Permission Copying without a fee is permitted provided that the copies are not made or distributed for direct commercial advantage, and credit to source is given. Abstracting is permitted with credit to the source. Please contact the publisher for other copying, reprint, or republication permission. © Copyright 2016 CSREA Press ISBN: 1-60132-437-5 Printed in the United States of America

CSREA Press U. S. A.

Foreword It gives us great pleasure to introduce this collection of papers to be presented at the 2016 International Conference on Health Informatics and Medical Systems (HIMS’16), July 25-28, 2016, at Monte Carlo Resort, Las Vegas, USA. An important mission of the World Congress in Computer Science, Computer Engineering, and Applied Computing (a federated congress to which this conference is affiliated with) includes "Providing a unique platform for a diverse community of constituents composed of scholars, researchers, developers, educators, and practitioners. The Congress makes concerted effort to reach out to participants affiliated with diverse entities (such as: universities, institutions, corporations, government agencies, and research centers/labs) from all over the world. The congress also attempts to connect participants from institutions that have teaching as their main mission with those who are affiliated with institutions that have research as their main mission. The congress uses a quota system to achieve its institution and geography diversity objectives." By any definition of diversity, this congress is among the most diverse scientific meeting in USA. We are proud to report that this federated congress has authors and participants from 74 different nations representing variety of personal and scientific experiences that arise from differences in culture and values. As can be seen (see below), the program committee of this conference as well as the program committee of all other tracks of the federated congress are as diverse as its authors and participants. The program committee would like to thank all those who submitted papers for consideration. About 63% of the submissions were from outside the United States. Each submitted paper was peer-reviewed by two experts in the field for originality, significance, clarity, impact, and soundness. In cases of contradictory recommendations, a member of the conference program committee was charged to make the final decision; often, this involved seeking help from additional referees. In addition, papers whose authors included a member of the conference program committee were evaluated using the double-blinded review process. One exception to the above evaluation process was for papers that were submitted directly to chairs/organizers of pre-approved sessions/workshops; in these cases, the chairs/organizers were responsible for the evaluation of such submissions. The overall paper acceptance rate for regular papers was 26%; 18% of the remaining papers were accepted as poster papers (at the time of this writing, we had not yet received the acceptance rate for a couple of individual tracks.) We are very grateful to the many colleagues who offered their services in organizing the conference. In particular, we would like to thank the members of Program Committee of HIMS’16, members of the congress Steering Committee, and members of the committees of federated congress tracks that have topics within the scope of HIMS. Many individuals listed below, will be requested after the conference to provide their expertise and services for selecting papers for publication (extended versions) in journal special issues as well as for publication in a set of research books (to be prepared for publishers including: Springer, Elsevier, BMC journals, and others). x x x x

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Dr. Mohd Helmy Abd-Wahab (ABDA'16); Former Head of Intelligent System Lab., Senior Lecturer and Academic Advisor, Department of Computer Engineering, University Tun Hussein Onn Malaysia, Malaysia Prof. Abbas M. Al-Bakry (Congress Steering Committee); University President, University of IT and Communications, Baghdad, Iraq Prof. Nizar Al-Holou (Congress Steering Committee); Professor and Chair, Electrical and Computer Engineering Department; Vice Chair, IEEE/SEM-Computer Chapter; University of Detroit Mercy, Detroit, Michigan, USA Prof. Hamid R. Arabnia (Congress Steering Committee & Coordinator); Graduate Program Director (PhD, MS, MAMS); The University of Georgia, USA; Editor-in-Chief, Journal of Supercomputing (Springer); Editor-in-Chief, Transactions of Computational Science & Computational Intelligence (Springer); Fellow, Center of Excellence in Terrorism, Resilience, Intelligence & Organized Crime Research (CENTRIC). Prof. P. Balasubramanian (ESCS'16); School of Computer Engineering, Nanyang Technological University, Singapore Prof. Juan Jose Martinez Castillo; Director, The Acantelys Alan Turing Nikola Tesla Research Group and GIPEB, Universidad Nacional Abierta, Venezuela

x x x x x x x

x x

x x x x x x x x x x x x x x x x x x

Prof. Kevin Daimi (Congress Steering Committee); Director, Computer Science and Software Engineering Programs, Department of Mathematics, Computer Science and Software Engineering, University of Detroit Mercy, Detroit, Michigan, USA Prof. Leonidas Deligiannidis (Co-Editor); Department of Computer Information Systems, Wentworth Institute of Technology, Boston, Massachusetts, USA Dr. Lamia Atma Djoudi (Chair, Doctoral Colloquium & Demos Sessions); Synchrone Technologies, France Dr. Trung Duong (IPCV'16); Research Faculty at Center for Advanced Infrastructure and Transportation (CAIT), Rutgers University, the State University of New Jersey, New Jersey, USA Prof. Mary Mehrnoosh Eshaghian-Wilner (Congress Steering Committee); Professor of Engineering Practice, University of Southern California, California, USA; Adjunct Professor, Electrical Engineering, University of California Los Angeles, Los Angeles (UCLA), California, USA Dr. Jyoti Gautam; Head, Department of Computer Science and Engineering, JSS Academy of Technical Education, Noida, U.P., India Prof. George A. Gravvanis (Congress Steering Committee); Director, Physics Laboratory & Head of Advanced Scientific Computing, Applied Math & Applications Research Group; Professor of Applied Mathematics and Numerical Computing and Department of ECE, School of Engineering, Democritus University of Thrace, Xanthi, Greece; former President of the Technical Commission on Data Processing, Social Security for the Migrant Workers, European Commission, Hellenic Presidency, Greece Dr. Ruizhu Huang; Texas Advanced Computing Center, University of Texas, Austin, Texas, USA Prof. George Jandieri (Congress Steering Committee); Georgian Technical University, Tbilisi, Georgia; Chief Scientist, The Institute of Cybernetics, Georgian Academy of Science, Georgia; Ed. Member, International Journal of Microwaves and Optical Technology, The Open Atmospheric Science Journal, American Journal of Remote Sensing, Georgia Hamidreza Khataee (BIOCOMP'16); School of Information & Communication Technology, Griffith University, QLD, Australia Prof. Byung-Gyu Kim (Congress Steering Committee); Multimedia Processing Communications Lab.(MPCL), Department of Computer Science and Engineering, College of Engineering, SunMoon University, South Korea Prof. Tai-hoon Kim; School of Information and Computing Science, University of Tasmania, Australia Prof. D. V. Kodavade (ABDA'16); Professor & Head, Computer Science & Engineering Department, D.K.T.E Society's Textile & Engineering Institute, Ichalkaranji, Maharashtra State, India Prof. Dr. Guoming Lai; Computer Science and Technology, Sun Yat-Sen University, Guangzhou, P. R. China Prof. Hyo Jong Lee; Director, Center for Advanced Image and Information Technology, Division of Computer Science and Engineering, Chonbuk National University, South Korea Dr. Ying Liu (BIOCOMP'16); Division of Computer Science, Mathematics and Science, College of Professional Studies, St. John's University, Queens, New York, USA; Lifetime member of ACM. Dr. Muhammad Naufal Bin Mansor; Faculty of Engineering Technology, Kampus Uniciti Alam, Universiti Malaysia Perlis, UniMAP, Malaysia Dr. Andrew Marsh (Congress Steering Committee); CEO, HoIP Telecom Ltd (Healthcare over Internet Protocol), UK; Secretary General of World Academy of BioMedical Sciences and Technologies (WABT) a UNESCO NGO, The United Nations Prof. Ali Mostafaeipour; Industrial Engineering Department, Yazd University, Yazd, Iran Prof. James J. (Jong Hyuk) Park (Congress Steering Committee); Department of Computer Science and Engineering (DCSE), SeoulTech, Korea; President, FTRA, EiC, HCIS Springer, JoC, IJITCC; Head of DCSE, SeoulTech, Korea Prof. Shashikant Patil; Electronics & Telecommunication Engineering Department, Head of SVKMs NMiMS Bosch Rexroth Center of Excellence in Automation Technologies, Shirpur Campus, India Prof. Dr. R. Ponalagusamy (BIOCOMP'16); Department of Mathematics, National Institute of Technology, Tiruchirappalli, India Prof. Benaoumeur Senouci (ESCS'16); Embedded Systems Department, LACSC Laboratory- Central Electronic Engineering School-ECE Paris, Graduate School of Engineering, ECE Paris, Paris, France M. Shojafar (GCC'16); Department of Information Engineering Electronics and Telecommunications (DIET), University Sapienza of Rome, Rome, Italy Dr. Akash Singh (Congress Steering Committee); IBM Corporation, Sacramento, California, USA; Chartered Scientist, Science Council, UK; Fellow, British Computer Society; Member, Senior IEEE, AACR, AAAS, and AAAI; IBM Corporation, USA Ashu M. G. Solo, (Publicity Chair), Fellow of British Computer Society, Principal/R&D Engineer, Maverick Technologies America Inc. Prof. Dr. Ir. Sim Kok Swee; Fellow, IEM; Senior Member, IEEE; Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia

x x x

x

x x x

Dr. Jaya Thomas; Department of Computer Science, State University of New York, Korea (SUNY Korea) and Department of Computer Science, Stony Brook University, USA Prof. Fernando G. Tinetti (Congress Steering Committee); School of Computer Science, Universidad Nacional de La Plata, La Plata, Argentina; Co-editor, Journal of Computer Science and Technology (JCS&T). Prof. Hahanov Vladimir (Congress Steering Committee); Vice Rector, and Dean of the Computer Engineering Faculty, Kharkov National University of Radio Electronics, Ukraine and Professor of Design Automation Department, Computer Engineering Faculty, Kharkov; IEEE Computer Society Golden Core Member; National University of Radio Electronics, Ukraine Prof. Shiuh-Jeng Wang (Congress Steering Committee); Director of Information Cryptology and Construction Laboratory (ICCL) and Director of Chinese Cryptology and Information Security Association (CCISA); Department of Information Management, Central Police University, Taoyuan, Taiwan; Guest Ed., IEEE Journal on Selected Areas in Communications. Prof. Mary Yang (BIOCOMP'16); Director, Mid-South Bioinformatics Center and Joint Bioinformatics Ph.D. Program, Medical Sciences and George W. Donaghey College of Engineering and Information Technology, University of Arkansas, USA Prof. Jane You (Congress Steering Committee & Vice-Chair of IPCV'16); Associate Head, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong Prof. Wenbing Zhao; Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, Ohio, USA; Program Chair of IEEE Smart World Congress (France)

We would like to extend our appreciation to the referees, the members of the program committees of individual sessions, tracks, and workshops; their names do not appear in this document; they are listed on the web sites of individual tracks. As Sponsors-at-large, partners, and/or organizers each of the followings (separated by semicolons) provided help for at least one track of the Congress: Computer Science Research, Education, and Applications Press (CSREA); US Chapter of World Academy of Science (http://www.worldcomp.org/) ; American Council on Science & Education & Federated Research Council (http://www.americancse.org/); HoIP, Health Without Boundaries, Healthcare over Internet Protocol, UK (http://www.hoip.eu); HoIP Telecom, UK (http://www.hoip-telecom.co.uk); and WABT, Human Health Medicine, UNESCO NGOs, Paris, France (http://www.thewabt.com/ ). In addition, a number of university faculty members and their staff (names appear on the cover of the set of proceedings), several publishers of computer science and computer engineering books and journals, chapters and/or task forces of computer science associations/organizations from 3 regions, and developers of high-performance machines and systems provided significant help in organizing the conference as well as providing some resources. We are grateful to them all. We express our gratitude to keynote, invited, and individual conference/tracks and tutorial speakers - the list of speakers appears on the conference web site. We would also like to thank the followings: UCMSS (Universal Conference Management Systems & Support, California, USA) for managing all aspects of the conference; Dr. Tim Field of APC for coordinating and managing the printing of the proceedings; and the staff of Monte Carlo Resort (Convention department) in Las Vegas for the professional service they provided. Last but not least, we would like to thank the Co-Editors and Associate Co-Editors of HIMS’16: Prof. Hamid R. Arabnia, Prof. Leonidas Deligiannidis, Prof. George Jandieri, Ashu M. G. Solo, and Prof. Fernando G. Tinetti. We present the proceedings of HIMS’16.

Steering Committee, 2016 http://www.worldcomp.org/

Contents SESSION: MEDICAL SYSTEMS AND DEVICES + MONITORING TOOLS + RELATED METHODOLOGIES AND ALGORITHMS Multidimensional Mobility Metric for Continuous Gait Monitoring using a Single Accelerometer Ik-Hyun Youn, Deepak Khazanchi, Jong-Hoon Youn, Ka-Chun Siu

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Recognition of Smoking Gesture Using Smart Watch Technology Casey Cole, Bethany Janos, Dien Anshari, James F. Thrasher, Scott Strayer, Homayoun Valafar

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64 Channel Digital Signal Processing Algorithm Development for 6dB Snr Improved Hearing Aids Soon Jarng, Mano Samuel, You Kwon, Jae Seo, Dong Jarng

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Research on Representation and Similarity Measurement of ECG Series Chunkai Zhang, Jingwang Zhang, Haodong Liu, Longfei Chen

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Diagnostic Tool Development on Embedded System for Heart Fitness Measurement Marton Aron Goda, Attila Tihanyi, Istvan Osztheimer

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The Research of Human Activity State Recognition Base on Acceletometers Chunkai Zhang, Jiayao Jiang, Guoquan Wang, Zhiliang Hu

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SESSION: HEALTH INFORMATICS AND RELATED ISSUES A Case Study of Nurses Perceptions and Attitude of Electronic Medical Records in Riyadh and 45 Jeddah's Hospitals Afrah Almutairi, Rachel McCrindle Bipolar Depression Druid: Wireless Technology Framework to Predict Bipolar Depression Arshia Khan, Rushmeet Bahra

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Active Workspaces: A Dynamic Collaborative Business Process Model for Disease Surveillance 58 Systems Robert Fondze Jr Nsaibirni, Eric Badouel, Gaetan Texier, Georges-Edouard Kouamou What Motivates High School Students to Take Precautions against the Spread of Influenza? A 65 Data Science Approach to Latent Modeling of Compliance with Preventative Practice William L. Romine, Tanvi Banerjee, William R. Folk, Lloyd H. Barrow Probabilistic Analysis of Contracting Ebola Virus using Contextual Intelligence Arjun Gopalakrishnan, Krishna Kavi

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An Accountable Access Control for E-health Clouds Maode Ma, Qianqian Zhao, Yuqing Zhang

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SESSION: HEALTHCARE AND PUBLIC HEALTH RELATED SYSTEMS e-Health Diaries for People at End-of-Life: 'A Crutch to Lean on' Carolyn Wilson, Paula Ormandy, Cristina Vasilica, Shahid Ali

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A Context Driven Human Activity Recognition Framework Shatakshi Chakraborty, Chia Y. Han, Xuefu Zhou, William G. Wee

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Measuring the Quality of Numeracy Skill Assessment in Health Domain Mandana Omidbakhsh, Olga Ormandjieva

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Dependability Evaluation of WBAN-based Home Healthcare Systems Using Stochastic Activity Networks Manuel Rios, Fredy Rivera

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Regulation-driven Verification of Vein-to-vein Blood Transfer Safety Noha Hazzazi, Duminda Wijesekera, Jasem Albasri

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Design of Health Care System for Disease Detection and Prediction on Hadoop Using DM Techniques Dingkun Li, Hyun Woo Park, Erdenebileg Batbaatar, Yongjun Piao, Keun Ho Ryu

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Telemedicine Aware Video Coding Under Very-Low Bitrates Zain Ul-Abdin, Muhammad Shafique, Muhammad Abdul Qadir

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A Bond Graph Approach for Modeling the Client-Therapist Relation Abdelrhman Mahamadi, Shivakumar Sastry

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A High-performance and Accurate Medicine Detection System Atif Ullah Baig, Cao An Wang

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Surgical Capacity Sharing and Cooperation in an Integrated Hospital System Min Luo, Xiaoqiang Cai

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SESSION: POSTER PAPERS MedInternet: Application of Artificial Intelligence for Medical Data Collection and Analysis Babak Murad-Kangarli

151

An Ontology-Based Data Warehouse for Diagnosis and Communication in Intensive Care Settings Jeroen de Bruin, Mohamed Mouhieddine, Christian Schuh, Michael Hiesmayr

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Estimating Energy Expenditure by Using Radial Basis Function Network for Health Monitoring Meina Li, Seokeun Park, Youn Tae Kim

155

Solution To Hit Connectivity & Effective Healthcare Data Use Barry Silbermann, Roger Blake, Stephen Berens

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Algorithm and Method for Automated Acquisition of Medical History Howard Schneider, Xie Li

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Algorithm and Method for Automated Processing of Medical E-mails Howard Schneider, Xie Li

161

Utilizing the Google Project Tango Tablet Development Kit and the Unity Engine for Image and Infrared Data-Based Obstacle Detection for the Visually Impaired Rabia Jafri, Rodrigo Louzada Campos, Syed Abid Ali, Hamid R. Arabnia

163

Exploring New Interactions for Querying a Tuberculosis Database Octavio Hector Juarez-Espinosa, Eric Engle, Andrei Gabrielian

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SESSION: LATE BREAKING PAPERS - HEALTH INFORMATICS AND HEALTH APPLICATIONS An AI Model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents Nicholas Boltin, Daniel Vu, Bethany Janos, Alyssa Shofner, Joan Culley, Homayoun Valafar

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Application of Data Science to Discover the Relationship between Dental Caries and Diabetes 176 in Dental Records German H. Alferez, Jany Jimenez, Hector Hernandez Navarro, Merari Gonzalez, Rusbel Dominguez, Alejandro Briones, Hector Hernandez Villalvazo Security and Privacy in Mobile Applications Azene Zenebe, Karishma Thakkallapally

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SESSION MEDICAL SYSTEMS AND DEVICES + MONITORING TOOLS + RELATED METHODOLOGIES AND ALGORITHMS Chair(s) TBA

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Multidimensional Mobility Metric for Continuous Gait Monitoring using a Single Accelerometer Ik-Hyun Youn1, Deepak Khazanchi1, Jong-Hoon Youn1, and Ka-Chun Siu2 College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA 2 College of Allied Health Professions, University of Nebraska Medical Center, Omaha, NE, USA

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Abstract – Mobility has been measured in the form of gait analysis – a method used to assess the physical functioning of a human. Many health-related clinical and pathological gait analysis applications have been proposed in the last decades. Gait analysis research has been predominantly conducted in laboratories in a discipline-specific manner. The main measurement approach is to monitor intrinsic gait patterns in natural settings such as home or work. Our proposed mobility assessment approach is to assess gait along three major gait dimensions: intensity, symmetry, and variability. This multidimensional metric uses a single accelerometer to assess the three dimensions so that the gait metric is continuously applied to mimic the natural human gait in daily life. Initial experimental results demonstrate its practical use in different gait patterns with various gait speeds. Keywords: Mobility metric, gait metric, accelerometer, continuous monitoring, gait speed

1

Introduction

Mobility in the recent past has predominantly been studied using gait analysis. Gait analysis is used to assess individual conditions that affect a person’s ability to walk. Gait research broadly incorporates quantification and interpretation of gait patterns. Gait analysis was initially developed to assess the subtle changes in human gait patterns that were usually not noticeable by the naked eye. Many measurement approaches and technologies such as infrared cameras and pressure sensor platforms have been used to analyze human gait. For example, in typical gait analysis studies, high-speed digital cameras placed around a walkway are used. Several reflexive markers are attached at many different locations of the body to track motions of the body while walking. Another well-known application of human gait analysis focuses on pathology. The pathological gait analysis measures reflect underlying symptoms of diseases such as cerebral palsy and stroke. This analysis can also be applied to rehabilitation engineering, sports training to improve performance, and biometric identification. In the last decade, gait analysis has become more popular in the general population, particularly because gait is now well known as a fundamental physical activity for maintaining health levels [2]. With the advent of wearable devices such as

Fitbit, many researchers are trying to quantify gait patterns outside of laboratories [13]. Continuous gait monitoring using wearable devices enables us to quantify gait parameters using natural gaits. Many of the available wearable sensors try to measure physical activity by using parameters such as duration, number of steps, and energy consumption. The main advantage of wearable devices is better portability for assessment of gait patterns. For instance, measuring the number of steps using a wearable device on the waist or wrist is a popular way to connect gait to better human physical health. Although gait features from wearable devices are able to represent mobility patterns, the information is still insufficient to apply to a comprehensive understanding of human mobility to make useful predictions of activity [7]. A major problem for people who investigate health issues is to continuously monitor gait functioning and to timely interpret the data to prevent loss of gait functional abilities and improve the quality of life [17]. Although proposed wearable sensor-based gait analysis approaches have introduced great portability to assess gait patterns, many of them are disciplinespecific. Moreover, the lack of attempts to comprehensively depict gait patterns has been observed [15]. To address this gap, in this paper we propose a comprehensive mobility evaluation metric for continuous gait monitoring using a single wearable sensor. The mobility metric consists of three gait dimensions: symmetry, variability, and intensity. The rest of the paper is organized as follows. Section 2 introduces the proposed mobility evaluation metric, which uses wearable sensor-based gait recognition and feature extraction techniques. In section 3, we describe a technique to acquire data on the multidimensional components of mobility. In the next section, we report on an experimental study to demonstrate the efficacy of this metric. In the last section, we discuss the experimental results and their implications.

2

Comprehensive Mobility Measurement

Human gait involves complicated sequential commands by neural control of locomotion to the body muscle [5]. As the center of gravity moves forward based on bipedal motion, potential energy is converted to kinetic energy. Maintaining efficient gait requires minimizing this complicated mechanism smoothly [6]. Based on the literature, human gait is generally

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defined as a bipedal movement with dynamic balance control [1 and 14]. We propose a comprehensive mobility assessment metric for continuous monitoring using a single accelerometerbased system. We conceptualize that the mobility patterns of a person can be described in terms of the three key dimensions described in Table 1. Table 1. Mobility dimensions. Dimension

Definition

Functionality

Intensity

Walking dynamism

Locomotive ability to walk

Symmetry

Bilateral gait balance control

Bilateral balance

Variability

Stride-to-stride variation

Natural fluctuation

Intensity Intensity describes the active locomotion of a person. As opposed to smooth wheel motion, bipedal human movement generates dynamic and cyclic behaviors. We define active locomotion as a degree of dynamic gait. This functioning eventually enables a person to reach an intended location with a certain degree of dynamic locomotion. By considering the activeness of gait, we are able to measure degrees of active locomotion that describe how active the gait is. If we solely consider gait intensity, a higher degree of intensity demonstrates a better ability to actively propel a center of gravity forward.

natural variations that occur in locomotive performance for multiple gait cycles [8]. Variability in gait is a useful indicator of impaired locomotor control in a clinical study and a parameter in the evaluation of mobile abnormality [4]. The ability to mitigate gait variability within certain criteria is important in order to maintain effective mobility. Generally, increased variability in a movement pattern indicates less cooperative behavior among the components of the underlying control system. Decreased variability generally indicates highly stable and cooperative behavior [12].

3

Measurement of Mobility Dimensions

Each mobility dimension is measured using attributes collected from our wearable sensor system. Because a single accelerometer is used for the wearable measurement, attributes are derived from three-dimensional acceleration to assess gait parameters.

2.1.1

Symmetry Symmetry is a dimension that reflects the degree of bilateral balance of gait. Symmetry has been defined as a good balance between the actions of the limbs [3 and 11]. When a person has the capability to locomote between two locations, there will be patterns of locomotion. The bilateral balance of each stride is one of the gait functionalities that represents bilateral balance while walking. Bilateral balance is considered to be an efficacious method to analyze the balance control ability of humans. Gait symmetry includes the effect of limb dominance and different levels of muscle contributions in gait in order to achieve control and propulsion in gait mechanisms [10]. Gait symmetry has been used to understand the risk for falls. When a difference between two successive bilateral gait parameters increases, we can interpret this tendency as a more asymmetric gait pattern in a stride.

3.1.1

Intensity The vector magnitude (VM) of 3-D acceleration is used to identify the intensity of gait. Although step time easily represents locomotive capability to get to a destination, measuring time to reach the intended destination is inappropriate for a continuous monitoring design. Instead of time to get to a destination, we propose to directly use the magnitude of acceleration while walking. In particular, by taking the vector magnitude of acceleration, we can measure pure acceleration generated for center of gravity propulsion. A person who generates greater intensity in each gait cycle will tend to have a higher center of gravity propulsion.

2.1.2

2.1.3

Variability As a person keeps maintaining gait cycles, there will be a natural fluctuation over multiple strides. The intrinsic strideto-stride fluctuation is natural due to bipedal movement, and it is also an important aspect of human gait characteristics. The gait cycle variation is considered as a signature of individual gait patterns or an indicator of malfunction of balance control. The dimension of variability can then be defined in terms of the stride-to-stride fluctuation in human gait patterns and the

3.1.2

Symmetry The symmetry index (SI) proposed by Robinson et al. is used to assess asymmetry of gait [9]. Using SI, we quantify left and right step time gap as a degree of gait symmetry. Although the symmetry relies on complicated mechanisms from several components of the body, comparing a temporal gait parameter that is the left and right step time of each stride is an effective way of measuring symmetry. 3.1.3

Variability Stride time fluctuation is used to evaluate gait variability. Since we define gait variability as a natural inconsistency of gait over multiple strides, the natural variation of a temporal gait parameter is efficiently represented by the accelerometerbased sensor system used to measure gait. The standard deviation (SD) of multiple stride times is used to calculate the degree of variability in this study.

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Mobility Metric Computation

Mobility in terms of the gait metrics proposed in the previous section is measured using a single sensor wearable system. The three-dimensional acceleration measured by the sensor is used to recognize each step and extract associated gait features. Initially, a gait recognition algorithm is applied to

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determine the initiation timing of each step. Once the algorithm detects gait cycles, gait parameters are extracted from each gait cycle. In order to make this work self-contained, we briefly revisit the technical achievements in our preliminary studies and relevant background information [16].

4.1

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recognized gait cycles are shown. The vertical dotted lines indicate calculated gait cycles. Horizontal distances of each vertical line as step times are computed at this phase in order to extract gait dimension.

Gait Recognition

For accurate gait cycle recognition, the gait recognition technique searches peaks stemming from heel-strike actions. In Figure 1, we observe regular peaks from heel strikes. The actions also substantially change jerk, which is defined as a change in the rate of acceleration as shown in Figure 2.

Figure 3. Recognized steps with vertical dotted lines. Basic gait parameters listed in Table 2 are used to determine gait dimensions in the proposed dimensions for mobility. The gait parameters are directly calculated from raw acceleration data. Table 2. Basic gait parameters extracted from sensor.

Figure 1. Raw acceleration data.

Type

Name

Description

Acceleration

Step acceleration

3-D acceleration of each step

Step time

Step duration

Individual step time

Stride time

Standard deviation

Duration of two successive step times

4.2

Mobility Dimension Extraction

The three mobility dimensions are eventually obtained at the end of the following extraction process. Using mathematical approaches briefly discussed in section 2 regarding gait attributes, the basic gait parameters are translated to associated mobility dimensions using the following equations (refer to Table 3). Table 3. Attributes of gait metric. Name

Attribute

Figure 2. Jerk data from acceleration. Compared to an acceleration pattern, a jerk pattern shows clear peak moments as shown in Figure 2. In particular, the anteroposterior directional jerk is used to identify each heel strike, rather than other directional jerks. In Figure 3,

Intensity

Vector magnitude of 3-D acceleration

Symmetry

Symmetry index of successive step time

Variability

Standard deviation of stride times

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Intensity is defined as an average vector magnitude of 3D acceleration for each step. Equation 1 below is used to calculate intensity using 3-D step acceleration. Equation 1 is used to calculate the average vector magnitude of each step, where m is the number of acceleration samples in each step and x, y, and z represent each directional acceleration.

1 m ¦ mi1

VM

x

2 i

 yi  zi 2

2



(1)

Symmetry is assessed using the symmetry index [Robinson, 1987] as shown in Equation 2. Symmetry of gait is calculated using two consecutive step times, where T odd is the odd number step time and T even is the even number step time.

Todd  Teven

SI

1 T  T even 2 odd

u 100

(2)

Twelve healthy subjects volunteered to participate in our experiment. Five male and seven female subjects with ages between 21 and 33 participated in the experiment. Subjects were required to walk a 30-meter long walkway in a building. They wore comfortable shoes for the experiments. Before the data collection, the subjects walked the walkway twice in order to determine their preferred walking speeds. Table IV shows five different gait speeds that were computed from their preferred gait speeds. A single accelerometer was worn at the lower back of the trunk in each subject.

5.2

Results

Using the data collected from our experiment, we analyzed gait acceleration for five different gait speeds. Figure 4 shows the typical acceleration patterns from the slowest, preferred, and fastest gait speeds as an example.

Finally, variability is computed using recognized stride times. We previously defined gait variability as a standard deviation of stride times. Stride times are entered into Equation 3 to compute the variability of gait. In this equation, T denotes stride time and T is the mean of the stride times.

SD

5

¦ (T  T )

2

(3)

N 1

Experimental Study

We conducted an experimental study using the proposed multidimensional mobility metric. We chose varied walking speeds as an experimental protocol to demonstrate the practical use of the proposed gait metric. Because previous studies found that gait patterns changed when walking speed changed, we hypothesized that the proposed gait metric would reflect previous findings at various walking speeds.

5.1

Protocol Table 4. Gait speed table. Gait Speed

Gait Speed Control

Slowest

40 percent slower speed than the preferred

Slower

20 percent slower speed than the preferred

Preferred

Self-determined gait speed

Faster

20 percent faster speed than the preferred

Fastest

40 percent faster speed than the preferred

Figure 4. Acceleration from three different gait speeds. The proposed comprehensive mobility measurement evaluated each gait pattern, Figures 5-7 illustrate these experimental results. As shown, five different gait speeds showed a distinctive status of gait using our proposed metric. Figure 5 shows the intensity pattern for each gait speed. Intensity highly correlates with gait speeds with clear boundaries between different speeds. For example, the intensity value for the fastest and faster gait speed distributes independently without overlap between them. Figures 6 and 7 show similar patterns along with gait speeds. Typically, both gait dimensions yield the highest values of symmetry and variability at the lowest gait speeds. The results reflect previous findings; slow walking requires more tight dynamic balance control than faster gait, hence a slow gait likely has more inconsistency.

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Conclusion

Gait analysis has attracted a great deal of interest in the domain of clinical and biomechanical research. Gait analysis using wearable devices provides continuous and repeatable results in daily activity with inexpensive cost and convenient portability [13]. However, using basic temporal and spatial gait parameters such as the number of steps, step length, and step duration are insufficient to get informative gait interpretation.

Figure 5. Intensity result for five different gait speeds.

We propose a multidimensional view of mobility that uses a gait metric that includes notions of balance control and gait agility. We conducted an experimental study for various gait speeds using the proposed concept. The results illustrate that different speed gait is efficiently identified using our comprehensive mobility metric. It addresses the inherent spatial limitation of laboratory-based gait analysis. Moreover, the mobility metric can be extended and applied to continuously tracking human gait in daily living and work settings. We expect that our wearable sensor-based mobility metric can be adopted for long-term mobility monitoring to monitor general gait patterns. As a next step of the study, the multidimensional mobility concept is being applied to a physical activity classification system in order to capture various types of gait in natural setting to assess daily and weekly gait pattern changes.

7

References

[1] S. Collins, A. Ruina, R. Tedrake and M. Wisse, "Efficient bipedal robots based on passive-dynamic walkers," Science, vol. 307, pp. 1082-1085, Feb 18. 2005.

Figure 6. Symmetry result for five different gait speeds.

[2] U.S. General, "Surgeon General’s report on physical activity and health. From the Centers for Disease Control and Prevention," JAMA, vol. 276, pp. 522, 1996. [3] G. Giakas and V. Baltzopoulos, "Time and frequency domain analysis of ground reaction forces during walking: an investigation of variability and symmetry," Gait Posture, vol. 5, pp. 189-197, 1997. [4] J.M. Hausdorff, "Gait variability: methods, modeling and meaning," Journal of Neuroengineering and Rehabilitation, vol. 2, pp. 1, 2005. [5] F. Lacquaniti, Y.P. Ivanenko and M. Zago, "Patterned control of human locomotion," J. Physiol. (Lond.), vol. 590, pp. 2189-2199, 2012. [6] D.W. Marhefka and D.E. Orin, "Gait planning for energy efficiency in walking machines," in Robotics and Automation, 1997 IEEE International Conference on, pp. 474-480, 1997.

Figure 7. Variability results for five different gait speeds.

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[7] C. Mummolo, L. Mangialardi and J.H. Kim, "Quantifying dynamic characteristics of human walking for comprehensive gait cycle," J.Biomech.Eng., vol. 135, pp. 091006, 2013.

[13] W. Tao, T. Liu, R. Zheng and H. Feng, "Gait analysis using wearable sensors," Sensors, vol. 12, pp. 2255-2283, 2012.

[8] M. Plotnik, N. Giladi and J.M. Hausdorff, "Bilateral coordination of gait and Parkinson's disease: the effects of dual tasking," J. Neurol. Neurosurg.Psychiatry, vol. 80, pp. 347350, Mar. 2009.

[14] M. Vukobratovic, B. Borovac, D. Surla and D. Stokic, Biped locomotion: dynamics, stability, control and application, Springer Science & Business Media, 2012.

[9] R.O. Robinson, W. Herzog and B.M. Nigg, "Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry," J. Manipulative Physiol. Ther., vol. 10, pp. 172-176, Aug. 1987. [10] H. Sadeghi, "Local or global asymmetry in gait of people without impairments," Gait Posture, vol. 17, pp. 197-204, 2003. [11] H. Sadeghi, P. Allard, F. Prince and H. Labelle, "Symmetry and limb dominance in able-bodied gait: a review," Gait Posture, vol. 12, pp. 34-45, 2000.

[15] S.C. Webber, M.M. Porter and V.H. Menec, "Mobility in older adults: a comprehensive framework," Gerontologist, vol. 50, pp. 443-450, Aug. 2010. [16] I. Youn, S. Choi, R. Le May, D. Bertelsen and J. Youn, "New gait metrics for biometric authentication using a 3-axis acceleration," in Consumer Communications and Networking Conference (CCNC), 2014 IEEE 11th, pp. 596-601, 2014. [17] W. Zijlstra and K. Aminian, "Mobility assessment in older people: new possibilities and challenges," European Journal of Ageing, vol. 4, pp. 3-12, 2007.

[12] N. Stergiou, R.T. Harbourne and J.T. Cavanaugh, "Optimal movement variability: a new theoretical perspective for neurologic physical therapy," Journal of Neurologic Physical Therapy, vol. 30, pp. 120-129, 2006.

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159

Algorithm and Method for Automated Acquisition of Medical History Howard Schneider 1, Xie Li2 1 Sheppard Clinic North, Toronto, Ontario, Canada 2 DocPod Corp, Toronto, Ontario, Canada

Abstract - A successfully implemented algorithm and method of obtaining a medical history automatically from a patient is described. The main algorithm consists of a probe question/questionnaire, a coarse prediction algorithm, a fine prediction algorithm and a medical history generating system. The coarse prediction algorithm uses simple pattern matching against the medical knowledge database. The fine prediction algorithm uses a combination of pattern matching against the medical knowledge database, Bayesian inference where probability values so allow, and deep learning (both unsupervised and supervised as there is always human physician oversight of the system) where the number of patients is high enough to reasonably allow. Keywords: Automated Medical History, Patient Computer Interview

In all areas of medicine, a medical history is the first step in arriving at a diagnosis and subsequently providing treatment to the patient. Traditionally, healthcare providers ask patients questions and in documenting the encounter generate a medical history. There are many problems with generating a medical history that is accurate, comprehensive and economical. Ramsey and colleagues [1] showed that of 134 primary care physicians studied, the physicians only asked 59% of what would be considered essential questions in order to obtain an accurate history from the patient. Tang and colleagues [2] showed that physicians in ambulatory practices spent one-fifth of their day writing. Thus there is a high cost in relatively expensive physician time being spent on charting. As early as the 1960’s physicians were trying to use computers to solve the problems of generating a medical history that is accurate, comprehensive and economical [3]. Nonetheless, despite the advancements and improvements in computer

technology over the decades, at the time of this writing, few physicians or other health care providers make use of automated medical history systems. Bachman in 2003[4] considers why physicians may not want to use computer-based interviewing, and in particular notes, “A computer program does not necessarily distinguish background symptoms from those leading to a visit to a physician. The physician, the patient, or the software needs to determine what is relevant.” Much of the research in artificial intelligence in medical diagnosis assumes that there is an existing data set of input data. However, to the clinician seeing patients day after day the main issue is not making a diagnosis but the large amount of time and effort and skill that is required to obtain this input data from the patient. We describe a successful algorithm and method of obtaining this input data, ie, obtaining a medical history from a patient, that has emerged from our pilot project in automated medical history generation. In 2015 110 patients in a general psychiatry clinic used our pilot automated system which in turn generated semi-automated and automated medical psychiatric histories which were compared to medical psychiatric histories generated conventionally, and the algorithm and method incrementally improved with each cohort of patients. The main algorithm the system used was as follows: 1. Obtain previous medical history 2. Obtain probe question answer from patient “Why are you here?” 3. Obtain medical knowledge database related to #1 and #2

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4. From coarse prediction algorithm and #1, #2 and #3, ask patient next question 5. If response to question from #4 was not appropriate try #4 again 6. From fine prediction algorithm ask patient questions and obtain patient data (weight, blood pressure, lab values, etc) related to likely diagnoses. 7. If answers to #6 indicate another diagnosis repeat again from #4 8. Generate structured medical history appropriate for visit and likely diagnosis The coarse prediction algorithm uses simple pattern matching against the medical knowledge database. The fine prediction algorithm uses a combination of pattern matching against the medical knowledge database, Bayesian inference where probability values so allow, and deep learning (both unsupervised and supervised as there is always human physician oversight of the system) where n (number of patients) is high enough to reasonably allow.

References [1] Ramsey PG, Curtis JR, Paauw DS, Carline JD, Wenrich MD. History-taking and preventive medicine skills among primary care physicians: an assessment using standardized patients. Am J Med. 1998;104:152-158. [2] Tang PC, Jaworski MA, Fellencer CA, et al. Methods for Assessing Information Needs of Clinicians in Ambulatory Care. Proc Annu Symp Comput Appl Med Care. 1995; 630-634. [3] Mayne JG, Weksel W, Sholtz, PN. Toward automating the medical history. Mayo Clin Proc. 1968 Jan;43(1):1-25. [4] Bachman JW. “The Patient-Computer Interview: A Neglected Tool That Can Aid the Clinician. Mayo Clin Proc. 2003;78:67-78.

The system was implemented with a cloud architecture (Google App Engine) with physician portals and patient entry programs all running in web browsers independent of the underlying computer hardware. Due to local regulatory concerns no medical devices were directly interfaced to the system, but relied on the patient or the physician to input physical exam and lab values. Future work includes comparing the time the physician must spend obtaining a medical history standardized to a certain level of quality and other time with the patient versus the time the physician needs to spend with the patient where an automated medical history is obtained, and comparing such comparisons against n (number of patients) for that diagnosis, with the expectation that time savings will increase as the system sees more patients of a given diagnosis. Ethics Review Board Approval: Canadian SHIELD Ethics Review Board #14-03-002

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161

Algorithm and Method for Automated Processing of Medical E-mails Howard Schneider 1, Xie Li2 1 Sheppard Clinic North, Toronto, Ontario, Canada 2 DocPod Corp, Toronto, Ontario, Canada

Abstract - We describe an algorithm and method for an

Intelligent Personal Assistant (IPA) for a health care practitioner such that the IPA can automatically schedule appointments with patients, automatically schedule appointments with colleagues and for other third party events, and automatically respond to routine communications from patients, from colleagues and from other third parties. The algorithm was implemented using a commercially available cloud architecture, commercial scheduling system, and commercial voicemail to e-mail conversion system. This algorithm and method has the potential to automate many of the personal assistant tasks required in a medical practice and to correspondingly lower the cost of health care. Keywords: Intelligent Personal Assistant, AI Assistant

E-mails are increasingly becoming a routine feature of medical practice. Medical e-mails may be received from patients [1], from other colleagues, from assistants, from laboratories, from medical marketing, as well as from non-medical sources. Time spent by the health care practitioner in responding to e-mails, is time that is in short supply and must be rationed with patients [2]. Intelligent Personal Assistants (‘IPA’s) are software agents that can provide some or many of the tasks an individual requires in managing one’s life, or in the case of a health care practitioner, in managing a medical practice, such as scheduling time with patients, scheduling time with colleagues and third party contacts (eg, educational events), and responding to routine phone calls and e-mails. Work has been done on IPAs for the last several decades, with the resultant systems becoming more interactive and arguably useful as time has progressed [3,4,5,6]. However, at the time of this writing, no IPA commercially available can actually meet the ‘reasonable personal assistant’ needs of a health care practitioner, which we can define as follows:

1. Automatically schedule appointments with patients 2. Automatically schedule appointments with colleagues and other third party events 3. Automatically respond to routine communications from patients 4. Automatically respond to routine communications from colleagues and other third parties We describe a successful algorithm and method of providing the above defined ‘reasonable personal assistant’ needs of a health care practitioner. Steps 5 and 6 of the algorithm below make use of another algorithm [7] which allows automated acquisition of medical histories. The main algorithm the system uses is as follows: 1. Obtain Input Data: Receive e-mails or convert voicemails/phone calls to e-mail. 2. Context Awareness: An attempt to make assumptions about a sender’s message (rather than the usual environmental interpretation of context, eg, Yan et al [8] versus Dey [9]) : Parse e-mail k times (initial value adjusted by a supervised learning algorithm depending on the health care practitioner’s satisfaction of the result in the overall handling of a communication) and select the parsed version which maximizes a score with regard to the version being in the context of: -a patient requesting an appointment/change -or a colleague/third party requesting a meeting/change -or a communication from a patient requiring a medical response -or a communication from a colleague/third party requiring a medical response -or an ‘other’ e-mail (which includes e-mails explicitly stating that a human response is required) The algorithm has access to the e-mail contact list of the health care practitioner as well as the medical records of the health care practitioner in order to best calculate this contextual score.

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3. If #2 ‘Context Awareness’== ‘patient requesting an appointment/change’ then the following will occur: i. If ‘no urgency’ then: An appointment will be given at the next available normal empty appointment slot for the health care practitioner. Feedback will be asked of the patient – if patient responds that the appointment is ok or does not respond then the algorithm terminates, or if patient responds that a different time or date is required, then an alternative appointment will be given to the patient and feedback is again asked of the patient. ii. If ‘urgency’ detected in #2 then: The original e-mail will be forwarded to the health care practitioner, and the algorithm terminates. 4. If #2 ‘Context Awareness’== ‘colleagues/third parties requesting an appointment/change’ then the following will occur: i. If ‘no urgency’ then: Similar to 3i. ii. If ‘urgency’ detected in #2 then: Similar to 3ii. 5. If #2 ‘Context Awareness’== ‘routine communications from patients’ then the following will occur: i. If the patient does not exist in the health care practitioner’s medical records database then a message is sent to the patient that it is not possible to directly respond until the sender becomes an official patient of the health care practitioner and instructions how to do so. ii. If a patient is an existing patient then the same probe questionnaire that is used in clinic by the automated medical acquisition history system (ie, 2016 Schneider and Xie [7]) is sent to the patient, and in response to the patient’s responses follow-up questionnaires are sent until either an ‘action plan/learning questionnaire’ is sent to the patient or the questionnaire data and e-mail are forwarded to the health care practitioner for human response, and the algorithm terminates. 6. If #2 ‘Context Awareness’== ‘routine communications from colleagues/third parties’ then the following will occur: Similar to 5ii but a colleague/third party probe questionnaire is used to better determine the exact request of the colleague/third party. 7. If #2 ‘Context Awareness’== ‘other e-mail’ then the following will occur: i. If an alternative external IPA system is being used, then the original e-mail is submitted to the alternative external IPA system, and this algorithm terminates. ii. If no alternative external IPA system is being used, then the original e-mail is submitted to the health care practitioner for manual processing, and this algorithm terminates.

The system was implemented with a cloud architecture (Google App Engine) with a commercially available scheduling system (Google Calendar). A commercially available voicemail to email conversion system was used (Vonage). At the time of this writing the system is being used in a general psychiatry clinic with most but not all modules above implemented. For example, there is no

external IPA being used. At the time of this writing the system is able to replace greater than 70% of the labor spent by a human assistant in a full-time position in a similar role in a medical practice. This algorithm and method has the potential to automate many of the personal assistant tasks required in a medical practice and to correspondingly lower the cost of health care.

References [1] Skerrett PJ. “Doctors debate use of email for communicating with their patients”, Harvard Medical School Harvard Health Blog. Jan 26 2012. Retrieved from : http://www.health.harvard.edu/blog/doctors-debate-use-ofemail-for-communicating-with-their-patients-201201264155 [2] Dugdale DC, Epstein R, and Pantilat SZ. “Time and the Patient-Physician Relationship”, J Gen Intern Med. 14(Suppl 1): S34-S40 Jan 1999. [3] Erol K, Hendler J and Nau D. “Semantics for Hierarchical Task-Network Planning”, Technical Report CSTR-3239, Computer Science Department, University of Maryland 1994. [4] Myers K, Berry P, Blythe J et al. “An Intelligent Personal Assistant for Task and Time Management”, AI Magazine v28(2) 2007. [5] Magee C. “Meet Genee, Your Artifically Intelligent Personal Assistant”, TechCrunch, Aug 12 2015. Retrieved from : http://techcrunch.com/2015/08/12/meet-genee-yourartificially-intelligent-personal-assistant/ [6] Corbyn Z. “Meet Viv: the AI that wants to read your mind and run your life”, The Guardian Jan 31 2016. Retrieved from : https://www.theguardian.com/technology/ 2016/jan/31/viv-artificial-intelligence-wants-to-run-your-lifesiri-personal-assistants [7] Schneider H and Xie L. “Algorithm and Method for Automated Acquisition of Medical History”, 2nd International Conference on Health Informatics and Medical Systems 2016 (in press). [8] Yan C, Fan Z and Huang L, “DRWS: A Model for Learning Distributed Representations for Words and Sentences”, in Pham D and Park S (eds) PRICAI 2014: Proceedings 13th Pacific Rim International Conference on Artificial Intelligence pp 196-207, Springer, Switzerland, 2014. [9] Dey, AK. “Understanding and Using Context”, Personal and Ubiquitous Computing Vol 5(1):4-7, Feb 2001.

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Utilizing the Google Project Tango Tablet Development Kit and the Unity Engine for Image and Infrared Data-Based Obstacle Detection for the Visually Impaired Rabia Jafri1, Rodrigo Louzada Campos2, Syed Abid Ali3 and Hamid R. Arabnia2 Department of Information Technology, King Saud University, Riyadh, Saudi Arabia 2 Department of Computer Science, University of Georgia, Athens, Georgia, U.S.A. 3 Araware LLC, Wilmington, Delaware, U.S.A

1

Abstract: A novel image and infrared data-based application to assist visually impaired (VI) users in detecting and avoiding obstacles in their path while independently navigating indoors is proposed. The application will be developed for the recently introduced Google Project Tango Tablet Development Kit equipped with a powerful graphics processor and several sensors which allow it to track its motion and orientation in 3D space in real-time. It will exploit the inbuilt functionalities of the Unity engine in the Tango SDK to create a 3D reconstruction of the surrounding environment and to detect obstacles. The user will be provided with audio feedback consisting of obstacle warnings and navigation instructions for avoiding the detected obstacles. Our motivation is to increase the autonomy of VI users by providing them with a real-time mobile stand-alone application on a cutting–edge device, utilizing its inbuilt functions, which allows them to micro-navigate independently in possibly unfamiliar indoor surroundings. Keywords: Obstacle detection, obstacle avoidance, Unity, Project Tango, blind, visually impaired.

1. Introduction One of the major challenges faced by visually impaired (VI) individuals during navigation is detecting and avoiding obstacles or drop-offs in their path. RGB image and infrared data-based systems have emerged as some of the most promising solutions for addressing this issue; however, currently such systems fall short in terms of accurately localizing the user and providing real-time feedback about obstacles in his path. The Project Tango Tablet Development Kit [1], recently introduced by Google Inc., is an Android device, equipped with a powerful graphics processor (NVIDIA Tegra K1 with 192 CUDA cores) and several sensors (motion tracking camera, 3D depth sensor, accelerometer, ambient light sensor, barometer, compass, GPS, gyroscope), which allow it not only to track its own movement and orientation through 3D space in real time using computer vision techniques but also enable it to remember areas that it has travelled through and localize the user within those areas to up to an accuracy

of within a few centimeters. Its integrated infra-red based depth sensors also allow it to measure the distance from the device to objects in the real world providing depth data about the objects in the form of point clouds. The depth sensors and the visual sensors are synchronized, facilitating the integration of the data from these two modalities. Our project aims to utilize the Project Tango tablet to develop a system to assist VI users in detecting and avoiding obstacles in their path during navigation in an indoors environment. The system will exploit the inbuilt functionalities of the Unity engine in the Project Tango SDK to create a 3D reconstruction of the surrounding environment and to detect obstacles in real-time. The user will be provided with audio feedback consisting of obstacle warnings and navigation instructions for avoiding the detected obstacles. Our motivation is to increase the autonomy of VI users by providing them with a real-time mobile assistive stand-alone application on a cutting–edge device, utilizing its inbuilt functions, which allows them to micro-navigate independently in possibly unfamiliar indoor surroundings. The rest of the paper is organized as follows: Section 2 provides a brief overview of existing image and infrared databased obstacle detection and avoidance systems for the VI. Section 3 describes the application and outlines the plan for its development and evaluation. Section 4 concludes the paper.

2. Related work Newly emerging sensor technologies, such as Microsoft’s Kinect, Occipital’s Structure Sensor, and, most recently, Google’s Project Tango Tablet Development Kit [1], are making it possible to exploit infrared light to extract 3D information about the environment without the need to install any equipment in the surroundings. Recent development work on obstacle detection for the VI has specially focused on Kinect, either utilizing the data from its depth sensor alone or from both its RGB and depth sensors. However, since Kinect is not designed to be a wearable or handheld device, affixing it to the body or clothing results in bulky and aesthetically unappealing contraptions; furthermore, the Kinect sensor needs to be connected to a

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backend server making systems based on it vulnerable to communication and speed performance issues. The Project Tango tablet appears to have a distinct advantage over Kinect in that it is an aesthetically appealing, handheld, mobile device equipped with a powerful processor enabling it to execute computationally intensive code in real-time without the need to connect to a backend server. Moreover, it has several additional embedded sensors and in-built functionalities, which can be utilized for extending and improving the obstacle detection application in the future. A few preliminary applications for the Tango tablet have already been proposed for obstacle detection and avoidance for the VI: The system presented by Anderson [2] collects depth information about the environment, saves it in a chunkbased voxel representation, and generates 3D audio for sonification which is relayed to the VI user via headphones to alert him to the presence of obstacles. Wang et al. [3] cluster depth readings of the immediate physical space around the users into different sectors and then analyze the relative and absolute depth of different sectors to establish thresholds to differentiate among obstacles, walls and corners, and ascending and descending staircases. Users are given navigation directions and information about objects using Android’s text-to-speech feature. However, both these applications need further development and are yet to be tested with the target users [4].

3. Application Development The application is being developed for Google’s Project Tango Tablet Development Kit which is a 7” Android-based tablet. It will utilize the Project Tango Unity SDK [21] to acquire a 3D reconstruction of the surrounding environment in the form of a mesh which is created and updated in realtime. A character object in the 3D reconstruction will represent the user’s position in the real world. Obstacle warnings will be issued if the distance between the character object and any object in the 3D reconstruction becomes less than a certain threshold (initially, this will be set to 0.5 meters; however, this value may be modified or a usercontrolled customization option may be provided based on the results of our interviews with the target users). Feedback to the users will be provided by playing prerecorded audio files via an open-ear bone conduction Bluetooth headset. The feedback will include warnings about approaching obstacles - potentially including some details about their sizes and their positions relative to the user - and navigation instructions to avoid the obstacles (bear right, bear left, etc.). For users with some residual vision, a visual display option may also be provided in addition to the audio output. Adopting a user-centered design approach, we are planning to conduct semi-structured interviews with VI individuals in Athens, Georgia, USA in order to gain some insight into their preferences for the user interface of the application and to procure their suggestions for what features

should be included. The results of the interviews will inform the design of the system during its development. Once an initial prototype has been developed, the two main parts of the system - the obstacle detection component and the user interface – will be empirically evaluated. The performance of the obstacle detection component will be tested for obstacles of various sizes at various positions with respect to the user and under different lighting conditions. The user interface will be designed in accordance with the users’ preferences (acquired via the interviews) and will then be evaluated by conducting usability tests with VI users. A similar system, being developed in parallel for obstacle detection and avoidance for the VI using the Project Tango Tablet Development Kit, was introduced in [4]. However, this system employs a different approach for detecting the obstacles by directly segmenting the point cloud data acquired from the scene. We aim to eventually conduct a comparative evaluation of the proposed system with this one to study any differences in terms of speed, accuracy and general usability.

4. Conclusion A novel image and infrared data-based application to assist VI users in detecting and avoiding obstacles in their path while independently navigating indoors has been proposed in this paper. The application will utilize the functionalities of the Unity SDK of the Google Project Tango Development Kit to provide an aesthetically acceptable, costeffective, portable, stand-alone solution for this purpose. A user centered approach would be adopted for the design and development, with semi-structured interviews being conducted with VI users at the initial stages of the development cycle to inform the interface design and usability testing with the target users being carried out at later stages with the initial prototype in order to identify any usability problems and to better adapt the system to the users’ needs.

References [1]"Google

Project Tango (https://www.google.com/atap/project-tango/)." [2]D. Anderson. (2015, Navigation for the Visually Impaired Using a Google Tango RGB-D Tablet. Available: http://www.dan.andersen.name/navigation-for-thevisually-impaired-using-a-google-tango-rgb-d-tablet/ [3]"Tiresias: An app to help visually impaired people navigate easily through unfamiliar buildings. HackDuke 2015. Available: http://devpost.com/software/tiresiasie0vum," 2015. [4]R. Jafri and M. M. Khan, "Obstacle Detection and Avoidance for the Visually Impaired in Indoors Environments Using Google’s Project Tango Device," in The 15th International Conference on Computers Helping People with Special Needs (ICCHP 2016), Linz, Austria, 2016.

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Exploring new interactions for querying a tuberculosis database Octavio Hector Juarez-Espinosa; Eric Engle; Andrei Gabrielian Computational Biology Biosciences Branch (OCICB) National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH) Rockville, MD [email protected], [email protected], [email protected]

Abstract—this paper describes a software prototype created to improve user queries of the NIAID TB Portals database. The goal is to take advantage of medical images, such as x-rays, as unique pieces of knowledge to search information in a database. The software is not meant to act as a diagnostic tool. Instead, the software investigates the idea that a user can query a system with tuberculosis (TB) data by selecting a single x-ray from the database or uploading a new x-ray, and using the image to search for similar x-rays. These search methods take seconds to compare thousands of images and enable real-time database retrieval. This prototype enables users to then explore and analyze the selected subsets of de-identified, anonymized and curated TB patient cases related to the similar x-rays. Keywords—tuberculosis; exploration

x-rays;

images;

similarity;

data

I. INTRODUCTION With the advance of computational technologies such as, image processing, databases, and network technologies the medical databases available are growing exponentially. To deal with these large databases researchers and business have adopted new technologies such as big data, data mining, and machine learning. Other groups focus their work in technologies where the user interaction combined with automatic methods improve data exploration tasks. For example, a user can start exploring a database by interacting with a graph and searching for more detail by selecting parameters or moving slide bars to filter the data sets. Having interactive and automatic tools to explore large datasets on specific diseases might lead to wise and more informed decisions in medicine. The NIAID TB Portals database with data on hundreds of patients with tuberculosis that contain x-rays, CT-scans, treatment plan data, sequencing

data, and final outcome, researchers can get a better understanding of this desease. Tuberculosis is a global problem; although in the USA the number of registered cases is minimal. There are some countries with high mortality with this disease[1]. “In 2014, 9.6 million people fell ill with TB and 1.5 million died from the disease [2].” This software prototype is part of the NIAID TB Portals program that seeks to empower clinicians, academic researchers, and the health care industry with advanced solutions in bioinformatics, information technology, and genomics toward improving TB patient diagnostics and treatment. This project involves several organizations in Belarus, Moldova, Georgia, Romania, and Azerbaijan, forming a virtual team with NIAID [3]. II. USING IMAGE SIMILARITY FOR QUERYING THE SYSTEM Content-based information retrieval (CBIR) is a technology that takes advantage of image properties to query a system. This area of research has been around for many years but there is no standard way to apply it to medical problems [4]. In [4], the authors enumerate the possible causes: the lack of productive collaborations between medical and engineering experts; the lack of effective representation of medical content by low-level mathematical features; the lack of thorough evaluation of CBIR system performance and its benefit in health care; and the absence of appropriate tools for medical experts to experiment with a CBIR application. This poster is not presenting a new algorithm for image similarity; instead it is a response to the motivation to experiment with the possible ways to query a tuberculosis database based on image content.

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to an EC2; a database with information about clinical data hosted on a EC2 instance; an image repository with 685 DCM files, which is hosted on a S3 instance; and a set of programs in Python and PHP that manage and respond user requests. End-users are able to access the software using a web browser as shown in Figure 1.

A. Indexing Images The pipeline to index the images consists of: preprocessing x-rays to improve contrast; segmenting [5]; and the extraction of features for every x-ray in the repository. The image features used for indexing consist of a histogram and the cooccurrence matrix [6].

Figure1. Global view of the user interacting with the system

Two scenarios are implemented: •

The user uploads the x-ray and gets a set of similar x-rays as can be seen in Figure 1.

•

The user selects an x-ray image from the database and retrieves similar images.

B. Search Images The user uploads the x-ray and the system computes the equalized histogram and segments the image. Next, the features are extracted. With the vector of features the system iterates over the index file and computes the distance to each x-ray. Finally, the software sorts the vector and returns a set of ten record identifiers and images with the best similarity scores. IV. FUTURE WORK This project will be improved by quantifying the impact of image-based queries in data exploration tasks. Algorithms for indexing and searching images will also be improved using optimal features to represent the images. ACKNOWLEDGMENT This project is possible thanks to the guidance and leadership of Michael Tartakovsky, Chief Information Officer, and Alexander Rosenthal, Chief Technology Officer at NIAID. REFERENCES [1] Stop TB Partnership. “Tuberculosis Profiles by Country.” http://www.stoptb.org/countries/tbdata.asp [2] World Health Organization. “Tuberculosis,” last modified March 2016. http://www.who.int/mediacentre/factsheets/fs104/en/ [3] Belarus Tuberculosis Portal. http://tuberculosis.by [4] Long, L. R., Antani, S., Deserno, T. M., & Thoma, G. R. (2009). “Content-Based Image Retrieval in Medicine: Retrospective Assessment, State of the Art, and Future Directions.” International Journal of Healthcare Information Systems and Informatics: Official Publication of the Information Resources Management Association, 4, 1, 2009, pp. 1–16.

Figure 2. Example of sequences of user interactions

With this subset of similar images a user can ask more questions of the system. For example: compare the treatment plan for every one of the cases retrieved. Also the user might be interested in comparing the final outcome for the treatment as described in Figure 2. Finally, the user might want to see[1] [5] Candemir, S. et al., "Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration," IEEE the evolution of tuberculosis in a specific patient by presenting Transactions on Medical Imaging, 33, 2, February 2014, pp. 577– the series of x-rays for specific case. 590.

III. OUR IMPLEMENTATION OF CBIR SYSTEM The software prototype has been developed using cloud technologies and it has the following components: a web server that host the application and similarity index allocated

[6] Kovalev, V., and Petrou, M. “Multidimensional Co-occurrence Matrices for Object Recognition and Matching.” Graphical Models and Image Processing, 58, 3, May 1996, pp. 187–197.

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SESSION LATE BREAKING PAPERS - HEALTH INFORMATICS AND HEALTH APPLICATIONS Chair(s) TBA

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ISBN: 1-60132-437-5, CSREA Press ©

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