Automation in Tele-Neurorehabilitation

Preface xiii About the Editors xv List of Contributors xvii 1 Functional Near-Infrared Imaging for Neurological Evaluation 1 Zengyong Li 1.1 Introduction 1 1.1.1 Brief Overview of Functional Near-Infrared Spectroscopy  1 1.2 Application of fNIRS in Neurological Evaluation 4 1.2.1 The Field of Cognitive Function Assessment 4 1.2.2 The Field of Motor Function Assessment 5 1.3 Chapter Summary 10 Reference List 10 2 Advancing Electroencephalogram in Tele-Neurorehabilitation: Integrating Wearable Technology and Multimodal Systems 14 Parikshat Sirpal and Yuan Yang 2.1 Introduction 14 2.2 EEG and Its Applications in Neurorehabilitation 16 2.3 EEG Devices for Tele-Neurorehabilitation: Clinical and Research Tools 18 2.4 Multimodal Approaches and Integration with EEG 20 2.5 Advancements in Machine Learning for EEG Analysis in Neurorehabilitation 22 2.6 Tele-EEG in Neurorehabilitation: Practical Applications 24 2.7 Recommendations for Implementing Tele-EEG Neurorehabilitation Systems in Clinical Practice 26 2.8 Case Studies and Clinical Outcomes in Tele-EEG Neurorehabilitation 27 2.8.1 Case Study 1: Tele-EEG for Stroke Rehabilitation and Neuroplasticity 27 2.8.2 Case Study 2: Multimodal EEG-fMRI Neurofeedback in Poststroke Rehabilitation 27 2.8.3 Case Study 3: Seizure Prediction Using Multimodal EEG-ECG-PPG Wearable Devices 28 2.8.4 Case Study 4: Tele-EEG Wearable Devices for Sleep Monitoring 28 2.8.5 Case Study 5: Tele-EEG for Cognitive Rehabilitation in Traumatic Brain Injury 29 2.8.6 Case Study 6: Tele-EEG for Cognitive and Emotional Rehabilitation in Multiple Sclerosis 29 2.9 Conclusion 30 2.10 Acknowledgments 30 References 30 3 Biomechanical Evaluation of Neuromusculoskeleton 39 Asta Kizyte, Zhongzheng Wang, and Ruoli Wang 3.1 Introduction 39 3.2 Skeletal Muscle Architecture 39 3.2.1 Measurement Using Ultrasound Imaging 40 3.2.2 Measurement Using MRI 43 3.2.3 Comparison between Ultrasound Imaging and MRI in Muscle Architecture Quantification 45 3.3 Intrinsic Properties of the Skeletal Muscle 45 3.3.1 Elastography 45 3.3.2 Intramuscular Fat Content 47 3.4 High-Density Electromyography 48 3.4.1 Fundamentals of HDEMG 48 3.4.2 Applications in Clinical Population 49 3.4.3 HDEMG-Based Force and Torque Prediction 51 3.4.4 Limitations of HDEMG 51 3.5 Future Perspectives in Tele-Neurorehabilitation 52 References 53 4 Evaluations of Cortico-Muscular Functional Connectivity: Methods and Applications 56 Yanhuan Huang, Bibo Yang, and Xiaoling Hu 4.1 Introduction 56 4.1.1 Brief Overview of Cortico-Muscular Functional Connectivity 56 4.1.2 Importance of Understanding the Relationship between the Brain and Muscles 56 4.2 Neurophysiological Basis of CMFC 57 4.2.1 Brief Explanation of Cortico-Muscular Pathways 57 4.2.2 Neural Oscillations and Their Role in Motor Coordination 58 4.2.3 Cortical and Muscular Regions Involved in Motor Control 58 4.3 Methodologies for Evaluating CMFC 59 4.4 Applications of CMFC Evaluation 60 4.4.1 Case Study One: CMC Patterns during Distal Finger Movements 60 4.4.2 Case Study Two: dCMC Patterns in Proximal-to-Distal Compensation 64 4.4.3 Case Study Three: CMC-EMG-Driven NMES-Robot System 67 4.5 Challenges 73 4.6 Conclusions 74 References 74 5 Machine Learning Methods for Functional Recovery Prediction and Prognosis 76 Liwen Zha, Minxin Chen, and James Chung-Wai Cheung 5.1 Introduction: Background and Driving Forces 76 5.2 Multiple Linear Regression 77 5.3 Gradient Boosting Machines 77 5.4 Artificial Neural Networks and Deep Learning  80 5.5 K-Nearest Neighbor 83 5.6 Lasso Regression 84 5.7 Support Vector Machine 85 5.8 Conclusion: Challenges and Future Direction 87 References 91 6 Mobile Upper Limb Robots for Self-Help Telerehabilitation 94 Legeng Lin, Chingyi Nam, Wei Rong, Waiming Li, Fuqiang Ye, Mankit Pang, Honwah Wai, and Xiaoling Hu 6.1 Introduction 94 6.2 Mobile Upper Limb Rehabilitation Robots 95 6.2.1 Design of the Hybrid Robotic System  96 6.2.2 Evaluation of the Assistive Capability of ENMS 99 6.3 Home-Based Self-Help Upper Limb Telerehabilitation 102 6.3.1 Functional Effectiveness of Self-Help Upper-Limb Telerehabilitation at Home 103 6.3.2 Independency in Self‑Help Upper Limb Telerehabilitation at Home 104 6.4 Future Trends and Research Directions 105 6.5 Conclusion 106 References 106 7 Translating Telerehabilitation after Stroke from Laboratory to Clinical Service 108 Wanyi Qing, Ching-Yi Nam, Harvey Man-Hok Shum, Marko Ka-Leung Chan, King-Pong Yu, Serena Sin-Wah Ng, Bibo Yang, and Xiaoling Hu 7.1 Introduction 108 7.2 Home Adaptation of Telerehabilitation for Wrist–Hand after Stroke 109 7.2.1 Translation Process from Lab-Based Training to Home-Based Telerehabilitation 110 7.2.2 Independency in Self‑Help Wrist–Hand Training at Home  112 7.2.3 Rehabilitation Effectiveness of the Home-Based Telerehabilitation 113 7.3 Clinical Integration of Wrist–Hand Poststroke Telerehabilitation 113 7.3.1 Translation Process from Lab-Based Telerehabilitation to Clinic-Based Telerehabilitation 114 7.3.2 Independency and Training Experience in Wrist–Hand Training at Home 117 7.3.3 Rehabilitative Effectiveness of Both Telerehabilitation Settings 118 7.4 Conclusion 120 References 120 8 Mobile Trans-Spinal Electrical Stimulation Therapy for Spinal Cord Injury 122 Md Akhlasur Rahman, Farjana Taoheed, Peter Sturgess, Alistair McEwan, and Monzurul Alam 8.1 Introduction: Background and Driving Forces 122 8.2 Trans-Spinal Electrical Stimulation Therapy 124 8.2.1 Trans-Spinal Direct Current Stimulation  125 8.2.2 Trans-Spinal Pulsed Current Stimulation 126 8.3 Perspectives of Trans-Spinal Electrical Stimulation Therapy for SCI 133 8.4 Portable, Wearable Trans-Spinal Electrical Stimulation Device 141 8.5 Potential for Home-Based Telerehabilitation 142 8.6 Safety, Accessibility, and Acceptability of Using tsES for Home-Based Telerehabilitation 145 References  148 9 Transcranial Electrical Stimulation in Remote Rehabilitation: Applications, Challenges, and Future Directions 156 Minmin Wang, Huilin Mou, Xu Xie, Yuchen Xu, Yufeng Zang, Xiangming Ye, and Shaomin Zhang 9.1 Introduction: Background and Driving Forces 156 9.2 Remote TES in Clinical Applications 158 9.2.1 Stroke Rehabilitation 158 9.2.2 Depression 159 9.2.3 Parkinson’s Disease 159 9.2.4 Multiple Sclerosis 160 9.2.5 Evaluation of TES Efficacy and Safety in Remote Rehabilitation 160 9.3 Remote TES Technology and Solution Design 161 9.3.1 Challenges in Remote Neurostimulation 161 9.3.2 Assessment of Intervention Effects 163 9.3.3 Combining TES with Other Training Modalities: Cognitive Training Integration 163 9.4 Ethical, Social, and Regulatory Considerations in Remote TES Applications 164 9.4.1 Informed Consent and Autonomy 164 9.4.2 Privacy and Data Security 165 9.4.3 Safety and Ethical Oversight 165 9.5 Future Directions 166 9.6 Conclusion 167 9.7 Acknowledgments 167 References 168 10 Virtual Reality for Telerehabilitation  172 Wei Xijun 10.1 Introduction 172 10.2 VR in Neurorehabilitation 174 10.3 Pilot Study on IVR-Based Rehabilitation 175 10.4 Comparison with Existing Literature 178 10.5 Movement Performance across Environments 178 10.6 Cognitive and Movement Relationships in IADLs 179 10.7 Correlations with Standardized Tests 180 10.8 Implications for Rehabilitation Practice 180 10.9 Conclusion 181 References 181 11 Application of Telerehabilitation in Tertiary Level Community Rehabilitation Center 183 Marko Ka-Leung Chan 11.1 Introduction of Tertiary Level Rehabilitation and Local Practice in Hong Kong 183 11.2 Introduction of Telerehabilitation and Its Application in Occupational Therapy 187 11.3 Need of Telerehabilitation in Hong Kong and Its Benefit 188 11.4 Need for Healthcare Professionals in Community Services: Home-Based Technology-Enhanced Cognitive Assessment Using Physiological Signals 189 11.5 Translation of Center-Based Service to Home-Based Service by Designing Appropriate Training System 190 11.6 Challenges for the Development of Telerehabilitation in the Community 192 11.7 Case Example of Telerehabilitation 193 11.8 Future Development of Telerehabilitation and the Application of AI 194 References 196 12 Commercialization of Telerehabilitation Robots 198 Fuqiang Ye, Chingyi Nam, and Ji Miao 12.1 Background 198 12.2 Commercialization of Telerehabilitation Robots 201 12.3 Case of Exoneuromusculoskeleton to Mobilexo Arm 203 12.3.1 Commercialization of Mobilexo Arm 204 12.3.2 Challenges and Solutions 206 12.4 Conclusion 207 References 208 13 The Practice and Application of Intelligent Rehabilitation in Clinical Practice 210 Minjie Bian, Yurong Mao, and Dong Feng Huang 13.1 Introduction 210 13.1.1 Definition of Intelligent Rehabilitation 210 13.1.2 Foundations of Intelligent Rehabilitation 211 13.1.3 Technologies Enabling Intelligent Rehabilitation 212 13.1.4 Evolution of Rehabilitation Technologies 214 13.2 Clinical Applications of Intelligent Rehabilitation 215 13.2.1 Neurological Rehabilitation 215 13.2.2 Musculoskeletal Rehabilitation 218 13.2.3 Pediatric Rehabilitation 219 13.2.4 Cardiopulmonary Rehabilitation 19 13.3 Smart Rehabilitation Clinics 220 13.4 Hybrid E-Rehabilitation Services 222 13.5 Development of Metaverse for Intelligent Healthcare 24 13.6 Challenges in Intelligent Rehabilitation and Healthcare Metaverse 225 13.6.1 Technological Barriers 225 13.6.2 Privacy and Ethical Concerns 226 13.7 Future Directions in Intelligent Rehabilitation and Smart Clinics 227 13.7.1 AI-Driven Predictive Models 227 13.7.2 Expansion of Metaverse Applications 227 13.8 Conclusion 228 References 228 Index 231

Biography

Xiaoling Hu is an associate professor in the Department of Biomedical Engineering at the Hong Kong Polytechnic University. She obtained her PhD in 2002 from the Chinese University of Hong Kong. Her research interests cover robotic Internet of Things (IoT) for telerehabilitation, artificial intelligence and data technology for automated theranostics, neural engineering for smart aging, and translation research in stroke rehabilitation.

Zengyong Li is a professor and the director of the Department of Rehabilitation Training at the National Research Center for Rehabilitation Technical Aids, Beijing. He obtained his PhD from Shanghai Jiao Tong University in 2003. His research interests are functional neuroimaging, neuromodulation for rehabilitation, intelligent assistive technology, and translational medicine.