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E-grāmata: Human Motion Capture and Identification for Assistive Systems Design in Rehabilitation

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  • Formāts: PDF+DRM
  • Izdošanas datums: 30-Apr-2021
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119515234
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Human Motion Capture and Identification for Assistive Systems Design in RehabilitationPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 30-Apr-2021
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119515234
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HUMAN MOTION CAPTURE AND IDENTIFICATION FOR ASSISTIVE SYSTEMS DESIGN IN REHABILITATION

A guide to the core ideas of human motion capture in a rapidly changing technological landscape

Human Motion Capture and Identification for Assistive Systems Design in Rehabilitation aims to fill a gap in the literature by providing a link between sensing, data analytics, and signal processing through the characterisation of movements of clinical significance. As noted experts on the topic, the authors apply an application-focused approach in offering an essential guide that explores various affordable and readily available technologies for sensing human motion.

The book attempts to offer a fundamental approach to the capture of human bio-kinematic motions for the purpose of uncovering diagnostic and severity assessment parameters of movement disorders. This is achieved through an analysis of the physiological reasoning behind such motions. Comprehensive in scope, the text also covers sensors and data capture and details their translation to different features of movement with clinical significance, thereby linking them in a seamless and cohesive form and introducing a new form of assistive device design literature. This important book:

  • Offers a fundamental approach to bio-kinematic motions and the physiological reasoning behind such motions
  • Includes information on sensors and data capture and explores their clinical significance
  • Links sensors and data capture to parameters of interest to therapists and clinicians
  • Addresses the need for a comprehensive coverage of human motion capture and identification for the purpose of diagnosis and severity assessment of movement disorders

Written for academics, technologists, therapists, and clinicians focusing on human motion, Human Motion Capture and Identification for Assistive Systems Design in Rehabilitation provides a holistic view for assistive device design, optimizing various parameters of interest to relevant audiences.

1 Introduction 1(22)
1.1 Human Body - Kinematic Perspective
1(2)
1.2 Musculoskeletal Injuries and Neurological Movement Disorders
3(5)
1.2.1 Musculoskeletal injuries
3(1)
1.2.2 Neuromuscular disorders
3(5)
1.3 Sensors in Telerehabilitation
8(8)
1.3.1 Opto-electronic sensing
8(3)
1.3.2 RGB camera and microphone
11(3)
1.3.3 Inertial measurement unit (IMU)
14(2)
1.4 Model-based State Estimation and Sensor Fusion
16(1)
1.4.1 Summary and challenges
16(1)
1.5 Human Motion Encoding in Telerehabilitation
17(3)
1.5.1 Human motion encoders in action recognition
17(1)
1.5.2 Human motion encoders in physical telerehabilitation
18(1)
1.5.3 Summary and challenge
19(1)
1.6 Patients' Performance Evaluation
20(3)
1.6.1 Questionnaire-based assessment scales
21(1)
1.6.2 Automated kinematic performance assessment
21(1)
1.6.3 Summary and challenge
22(1)
2 Kinematic Performance Evaluation with Non-wearable Sensors 23(52)
2.1 Introduction
23(1)
2.2 Fusion
24(16)
2.2.1 Introduction
24(2)
2.2.2 Linear model of human motion multi-Kinect system
26(2)
2.2.3 Model-based state estimation
28(1)
2.2.4 Fusion of information
28(1)
2.2.5 Mitigation of occlusions and optimised positioning
28(1)
2.2.6 Computer simulations and hardware implementation
29(11)
2.3 Encoder
40(17)
2.3.1 Introduction
40(2)
2.3.2 The two-component encoder theory
42(1)
2.3.3 Encoding methods
43(2)
2.3.4 Dealing with noise
45(2)
2.3.5 Complex motion decomposition using switching continuous hidden Markov models
47(1)
2.3.6 Canonical actions and the action alphabet
48(1)
2.3.7 Experiments and results
49(8)
2.4 ADL Kinematic Performance Evaluation
57(17)
2.4.1 Introduction
57(2)
2.4.2 Methodology
59(2)
2.4.3 Experiment setup
61(4)
2.4.4 Data analysis and results
65(9)
2.5 Summary
74(1)
3 Biokinematic Measurement with Wearable Sensors 75(24)
3.1 Introduction
75(1)
3.2 Introduction to Quaternions
75(1)
3.3 Wahba's Problem
76(5)
3.3.1 Solutions to the Wahba problem
77(1)
3.3.2 Davenport's q method
78(2)
3.3.3 Quaternion Estimation Algorithm (QUEST)
80(1)
Reference frame rotation in the QUEST method
80(1)
3.3.4 Fast optimal attitude matrix (FOAM)
81(1)
3.3.5 Estimator of the optimal quarternion (ESOQ or ESOQ1) method
81(1)
3.4 Quaternion Propagation
81(1)
3.5 MARG (Magnetic Angular Rates and Gravity) Sensor Arrays-based Algorithm
82(1)
3.6 Model-based Estimation of Attitude with IMU Data
82(3)
3.7 Robust Optimisation-based Approach for Orientation Estimation
85(2)
3.8 Implementation of the Orientation Estimation
87(1)
3.8.1 Extended Kalman filter-based approach
88(1)
3.8.2 Robust extended Kalman filter implementation
88(1)
3.8.3 Robust extended Kalman filter with linear measurements
88(1)
3.9 Computer Simulations
88(1)
3.10 Experimental Setup
89(2)
3.11 Results and Discussion
91(5)
3.11.1 Computer simulations
91(1)
3.11.2 Experiment
92(4)
3.12 Conclusion
96(3)
4 Capturing Finger Movements 99(34)
4.1 Introduction
99(3)
4.2 System Overview
102(1)
4.3 Accuracy Improvement of Total Active Movement and Proximal Interphalangeal Joint Angles
103(3)
4.4 Simulation
106(2)
4.5 Trial Procedure
108(1)
4.6 Results
109(3)
4.6.1 Concurrence validity
109(1)
4.6.2 Internal reliability
110(1)
4.6.3 Time efficiency
111(1)
4.7 Discussions
112(1)
4.8 Approaching Finger Movement with a New Perspective
113(3)
4.9 Reachable Space
116(3)
4.10 Boundary of the Reachable Space
119(4)
4.11 Area of the Reachable Space
123(3)
4.12 Experiments
126(2)
4.13 Results and Discussion
128(4)
4.14 Conclusion and Future Work
132(1)
5 Non-contact Measurement of Respiratory Function via Doppler Radar 133(66)
5.1 Introduction
133(2)
5.2 Fundamental Operation of Microwave Doppler Radar
135(5)
5.2.1 Velocity and frequency
135(3)
5.2.2 Correction of I/Q amplitude and phase imbalance
138(2)
5.3 Signal Processing Approach
140(6)
5.3.1 Respiration rate
140(2)
5.3.2 Extracting respiratory signatures
142(3)
5.3.3 Low-pass filtering (LPF)
145(1)
5.3.4 Discrete wavelet transform
145(1)
5.4 Common Data Acquisitions Setup
146(5)
5.5 Capturing the Dynamics of Respiration
151(5)
5.5.1 Normal breathing
151(1)
5.5.2 Fast breathing
151(1)
5.5.3 Slow inhalation-fast exhalation
152(1)
5.5.4 Fast inhalation-slow exhalation
152(1)
5.5.5 Capturing abnormal breathing patterns
152(1)
5.5.6 Breathing component decomposition, analysis and classification
153(3)
5.6 Capturing Special Breathing Patterns
156(17)
5.6.1 Correlation of radar signal with spirometer in tidal volume estimations
157(1)
5.6.2 Experiment setup
157(1)
5.6.3 Results
158(10)
5.6.4 Motion signature from Doppler radar
168(1)
5.6.5 Measurement of volume in (inhalation) and volume out (exhalation)
169(4)
5.7 Removal of Motion Artefacts from Doppler Radar-based Respiratory Measurements
173(11)
5.7.1 Experimental verification
175(1)
5.7.2 Results and discussion
176(7)
5.7.3 Summary
183(1)
5.8 Separation of Doppler Radar-based Respiratory Signatures
184(15)
5.8.1 Respiration sensing using Doppler radar
185(1)
5.8.2 Signal processing source separation (ICA)
185(2)
5.8.3 Experiment protocol for real data sensing
187(1)
5.8.4 Two simulated respiratory sources
188(2)
5.8.5 Experiment involving real subjects
190(5)
5.8.6 Separation of hand motion
195(2)
5.8.7 Conclusion
197(2)
6 Appendix 199(8)
6.1 Static Estimators
199(1)
6.1.1 Least-squares estimation
199(1)
6.1.2 Maximum likelihood estimation
199(1)
6.2 Model-based Estimators
200(1)
6.2.1 Kalman filter (KF)
200(1)
6.3 Particle Filter
201(6)
6.3.1 Robust filtering with linear measurements
203(1)
6.3.2 Constrained optimisation
204(3)
Bibliography 207(24)
Index 231
Pubudu N. Pathirana, PhD is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and received his PhD in Electrical Engineering from the University of Western Australia. He was a Postdoctoral Research Fellow at Oxford University, Oxford, England; a Research Fellow at the School of Electrical Engineering and Telecommunications at the University of New South Wales, Sydney, Australia; and a consultant to the Defense Science and Technology Organization (DSTO). Currently, he is a Professor and the Director of the Networked Sensing and Control group at the School of Engineering, Deakin University, Geelong, Australia. His current research interests include bio-medical assistive device design, human motion capture, mobile/wireless networks, rehabilitation robotics, and radar array signal processing.

Saiyi Li, PhD received his PhD from Deakin University in 2016 through a sponsorship by the NICTA (National Information Communications Technology Australia, now Data61). His research interests include rehabilitation engineering, signal processing, and machine learning.

Yee Siong Lee, PhD received his PhD from the Deakin University in 2016 through a sponsorship by NICTA. His research interests include biomedical applications and signals processing, machine learning, operations research, sensors networks, and radar signal processing.

Trieu Pham, PhD received his PhD through a sponsorship by NICTA from Deakin University, Victoria, Australia in 2017. His research interests include machine learning, signal processing, computer aided rehabilitation, and kinematics of human motion.
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