Wearable Systems Based Gait Monitoring and Analysis provides a thorough overview of wearable gait monitoring techniques and their use in health analysis. The text starts with an examination of the relationship between the human body’s physical condition and gait, and then introduces and explains nine mainstream sensing mechanisms, including piezoresistive, resistive, capacitive, piezoelectric, inductive, optical, air pressure, EMG and IMU-based architectures. Gait sensor design considerations in terms of geometry and deployment are also introduced. Diverse processing algorithms for manipulating sensors outputs to transform raw data to understandable gait features are discussed. Furthermore, gait analysis-based health monitoring demonstrations are given at the end of this book, including both medical and occupational applications. The book will enable students of biomedical engineering, electrical engineering, signal processing, and ergonomics and practitioners to understand the medical and occupational applications of engineering-based gait analysis and falling injury prevention methods.
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2.1 Physical Parameters of Gait |
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2.1.2 Mechanical Parameters |
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2.1.4 Electrical Parameters |
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2.2 The Influences of Age, Occupation, and Disease on Gait |
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2.2.1 Age-Related Gait Changes |
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2.2.2 Occupation-Related Gait Changes |
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2.2.3 Disease-Related Gait Changes |
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3 Gait Detection Technologies |
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3.1 Footprint and Bulky Systems |
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3.1.3 Motion Capture System |
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3.2.1 Wearable Plantar Pressure Detection Systems |
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4 Gait Analysis Algorithms |
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4.1 Plantar Pressure Distribution Interpretation |
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4.1.1 Plantar Stress Distribution Reconstruction |
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4.1.2 Pressure-Related Parameters Extraction |
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4.1.3 The Registration of Plantar Pressure Mapping |
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4.1.4 Classification Algorithms |
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4.2 EMG Pattern Recognition |
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4.2.1 Denoising Algorithms |
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4.2.2 Feature Extraction and Dimensionality Reduction Algorithms |
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4.2.3 Classification Algorithms |
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4.3 IMU-Based Motion Classification Algorithms |
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4.3.1 Feature Extraction and Dimensionality Reduction Algorithms |
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4.3.2 Classification Algorithms |
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4.3.3 Motion Simulation and Generation Algorithms |
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4.4.1 Plantar Pressure with EMG |
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4.4.2 Plantar Pressure with IMU |
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4.4.4 Fusion of Plantar Pressure, IMU, and EMG Sensors |
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4.4.5 Two Case Studies of Multisensory Fusion |
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5.1 Neural Disease Analysis |
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5.2 Orthopaedic Disease Analysis |
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5.2.2 Knee Osteoarthritis |
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5.2.4 Total Joint Replacement |
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5.3 Rehabilitation Progress Tracking |
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5.3.1 Post-stroke Rehabilitation Tracking |
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5.3.2 Falling Risk Prediction |
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5.3.3 Mental Illness Rehabilitation Tracking |
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5.4 The Internet of Health Things |
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| Index |
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Shuo Gao, Ph.D., is currently an associate professor at the Beihang University, Beijing, China. He received his Ph.D. in electrical engineering from the University of Cambridge and from 2017 to 2018 was a research associate with University College London, UK. His area of expertise is wearable-system development. Professor Gao has published over 70 articles as the first/last author for peer-reviewed journals and flagship conferences and has been awarded 30 patents in the United States, United Kingdom, and China. He is co-author of the books Touch-Based Human-Machine Interaction: Principles and Applications (Springer, 2021) and A Flexible Multi-Functional Touch Panel for Multi-Dimensional Sensing in Interactive Displays (Cambridge University Press, 2019). He serves on several IEEE technical committees in various capabilities and is a member of the Editorial Board for Materials, Frontiers in Electronics, and Semiconductor Science and Information Devices. In terms of industrial experience, he has worked as a hardware engineer at SIGPRO, Ottawa, Canada, a system engineer at Ciena Corporation, Ottawa, Canada, and a technique consultant at Cambridge Touch Technologies Inc., Cambridge, UK. He is an expert group member of BOE Technology Group Co., Beijing, China.
Jun-Liang Chen is a lead engineer at Smart Electronics Inc., Beijing, China. He received his bachelors and master degrees in engineering from Beihang University, Beijing, China. His research interests include piezoelectricity, circuit design, and signal processing and he has published more than 10 peer-reviewed research and review articles.
Yan-Ning Dai is a biomedical scientist at Smart Electronics Inc., Beijing, China. She received a bachelors degree with a double major in instrumentation engineering and biomedical engineering and a masters degree in engineering from Beihang University, Beijing, China. Her research focuses on flexible electronics for human-machine interaction and health monitoring. She has published more than 20 peer-reviewed articles and patents.
Boyi Hu, Ph.D., has been an assistant professor of industrial and systems engineering at the University of Florida since 2018. He received his Ph.D. degree from West Virginia University majoring in Ergonomics in 2016 and worked as a post-doc research fellow at Harvard T.H.Chan School of Public Health from 2016 to 2018. His research interests are ergonomics, biomechanics, system safety, and human-robot interaction. He has published over 40 peer-reviewed articles. As PI or Co-PI, his research has been funded by multiple US federal agencies, including the National Science Foundation, Department of Transportation, USDA National Institute of Food and Agriculture, and the National Institute for Occupational Safety and Health with the direct cost of over $4M in total. He has been serving as an associate editor for IEEE Transactions on Human-Machine Systems since 2020.
