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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