Technology-Enabled Motion Sensing and Activity Tracking for Rehabilitation

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Contents
Introduction
Part I Motion sensing technologies
1 Inertial measurement units
1.1 Accelerometer
1.2 Gyroscope
1.3 Magnetometer
1.4 Rehabilitation studies using IMUs
1.4.1 Studies using low-level IMUs
1.4.2 Studies using prepackaged professional sensors containing IMUs
1.4.3 Studies using consumer-grade devices containing IMUs
1.4.4 Studies using wearable trackers
2 Force and pressure sensing
2.1 Types of pressure sensors
2.1.1 Piezoelectric pressure sensors
2.1.2 Resistive pressure sensors
2.1.3 Capacitive pressure sensors
2.1.4 Optical pressure sensors
2.2 Applications in motion tracking for rehabilitation
2.2.1 Epionics SPINE system
2.2.2 Force plates
2.2.3 Smart insoles and smart shoes
2.3 Energy harvesting in smart shoes
3 E-Textile-based sensing
3.1 Conductive elastomer
3.1.1 Working principle
3.1.2 Attaching conductive elastomer to fabric
3.1.3 Motion tracking with conductive elastomer
3.1.4 New development
3.2 Commercial elastic sensors
3.3 Other approaches
4 Muscle activity sensing with myography
4.1 Electromyography
4.1.1 EMG in upper-extremity stroke therapy
4.1.2 EMG in recovery progress evaluation of anterior cruciate ligament reconstructed subjects
4.2 Machanomyography
4.3 Force myography
4.4 Optical myography
4.5 Summary
5 Vision-based motion sensing
5.1 Microsoft Kinect sensor
5.2 Feasibility studies of using Kinect in rehabilitation
5.3 Kinect-based systems in rehabilitation
5.3.1 Kinect-based system with visual feedback only
5.3.2 Kinect-based system with performance quality feedback
5.3.3 Integration of Kinect and other sensing modalities
5.4 Beyond Kinect
6 Instrumented gloves
6.1 Gloves based on IMUs
6.1.1 Calibration
6.1.2 Signal processing
6.1.3 Reference systems for evaluation
6.1.4 Accuracy evaluation
6.1.5 Repeatability and reliability evaluation
6.1.6 Classification of activities
6.2 Gloves based on flex sensors
6.3 Gloves based on optical sensors
6.3.1 FBG-based approach
6.3.2 Light-attenuation-based approach
6.3.3 Optical linear encoder
6.4 Gloves based on Hall effect
Part II Human motion recognition and exergames
7 Measurement of basic parameters
7.1 Mechanics of body movements
7.1.1 Anatomical planes
7.1.2 Joints and their movements
7.1.3 Range of motion
7.2 Joint angle measurement with various sensing modalities
7.2.1 Joint angle measurement with IMU
7.2.2 Joint angle measurement with Kinect
7.3 Measurement theories
7.4 Evaluating a new measurement instrument
7.4.1 Root mean square error
7.4.2 Student's t-test
7.4.3 Pearson's coefficient of correlation
7.4.4 Intraclass correlation coefficient
7.4.5 Bland–Altman limits of agreement
8 Machine-learning-based activity recognition
8.1 Data pre-processing
8.2 Data segmentation
8.3 Feature engineering
8.3.1 Feature extraction
8.3.2 Feature selection
8.4 Supervised machine learning
8.4.1 Mathematical model for supervised machine learning
8.4.2 Cross validation
8.4.3 Common supervised machine-learning models
8.4.4 Performance evaluation for classification
8.4.5 Performance evaluation for regression
8.5 Unsupervised machine learning
8.6 Deep learning
8.7 Assessment of rehabilitation exercises
8.7.1 Activity recognition
8.7.2 Performance quality assessment
8.7.3 Clinical assessment
9 Rule-based activity recognition
9.1 Ad hoc rule-based studies
9.2 General-purpose rule-based activity recognition
9.2.1 Rule encoding method
9.2.2 Real-time motion tracking
9.2.3 Fuzzy interference extension
10 Exergames
10.1 Commercial game-console-based exergames
10.1.1 Wii
10.1.2 Xbox
10.1.3 PlayStation
10.2 Custom-developed exergames
10.2.1 IMU
10.2.2 Kinect
10.2.3 Wii balance board
10.2.4 Mobile apps
Part III Technology-facilitated rehabilitation
11 Technology-facilitated physical rehabilitation
11.1 Framework for physical rehabilitation
11.2 Motor control and motor learning
11.3 Interventions for improve motor function
11.4 Technology in physical rehabilitation
11.4.1 Augmented reality in physical rehabilitation
11.4.2 Smartphone use in physical rehabilitation
12 Technology-facilitated occupational rehabilitation
12.1 Framework for occupational therapy
12.2 Occupational therapy for return to work
12.3 Technology in occupational therapy
12.3.1 Assistive technology
12.3.2 Telerehabilitation
12.3.3 Exergames
12.4 Tracking of activities of daily living
13 Technology-facilitated speech rehabilitation
13.1 Common speech-related disorders
13.1.1 Aphasia
13.1.2 Dysarthria
13.1.3 Apraxia of speech
13.1.4 Dyslalia
13.1.5 Hearing impairment
13.1.6 Resonance disorders
13.1.7 Cognitive communication disorders
13.1.8 Expressive disorders
13.1.9 Fluency disorders
13.1.10 Articulation disorders
13.2 Standard speech and language therapy
13.3 Lee Silverman Voice Treatment
13.4 Computer-based speech therapy
14 Technology-facilitated pulmonary rehabilitation
14.1 Clinical scales and tests in pulmonary rehabilitation
14.1.1 The Borg Rating of Perceived Exertion
14.1.2 Dyspnea ratings
14.1.3 TheWisconsin Upper Respiratory Symptom Survey
14.1.4 Numeric rating scale as a measure of dyspnea
14.1.5 Medical Research Council dyspnea scale
14.1.6 Functional independence measure
14.1.7 Cumulative illness rating scale
14.1.8 St. Georg's respiratory questionnaire
14.1.9 Feeling thermometer
14.1.10 The six-minute walk test
14.1.11 Short physical performance battery
14.1.12 Functional ambulation category
14.2 Exercise training
14.2.1 Endurance training
14.2.2 Interval training
14.2.3 Resistance/strength training
14.2.4 Upper limb training
14.2.5 Flexibility training
14.2.6 Neuromuscular electrical stimulation
14.2.7 Inspiratory muscle training
14.3 Pulmonary rehabilitation for COPD
14.3.1 Functional testing and measurement of physiological parameters
14.3.2 Telehealth
14.3.3 Technology-facilitated exercise training
14.3.4 Technology-facilitated self-management
14.4 Pulmonary rehabilitation for COVID-19
15 Technology-facilitated cognitive rehabilitation
15.1 The impact of physical activities on cognition for children and young adults
15.2 Technology-facilitated detection of mild cognition impairment and dementia
15.2.1 Video-based detection of MCI
15.2.2 MCI-detection via fully-instrumented smart home
15.2.3 MCI-detection via minimally instrumented smart home
15.2.4 MCI-detection via non-mobility IADL tracking
15.3 Cognitive rehabilitation for older adults
16 Technology-facilitated mental health rehabilitation
16.1 Regular physical exercises and mental health
16.1.1 Psychological mechanisms
16.1.2 Inflammatory mechanisms
16.1.3 Psychological mechanisms
16.2 Rehabilitation for patients with autism spectrum disorder
16.2.1 Clinical scales in ASD studies
16.2.2 Social attention
16.2.3 Imitation
16.2.4 Cognitive load
16.2.5 Facial expression and emotion recognition
16.2.6 Physical exercise-based intervention
16.3 Exercise-based intervention for patients with major depressive disorder
16.3.1 Clinical assessments in MDD studies
16.3.2 Supporting studies
16.3.3 Nonsupporting studies
16.4 Exercise-based rehabilitation for patients with post-traumatic stress disorder
Conclusion
References
Index
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