Introduction xix
Abhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHORE

Chapter 1. Physiotherapy Patient Records Enhanced with Natural Language Processing 1
Mandar MALAWADE and Rasika Ranjit CHAFLE

1.1. Introduction 2
1.2. Physiotherapy patient records: structure, content and challenges 3
1.3. Fundamentals of NLP in healthcare 6
1.4. Applications of NLP in physiotherapy records 9
1.5. Technological frameworks for NLP in physiotherapy 13
1.6. Clinical benefits and opportunities 17
1.7. Conclusion 18
1.8. References 19

Chapter 2. Neural Network Models for Optimizing Rehabilitation Timelines 23
Namrata KADAM and K. GAVHALE

2.1. Introduction and background 23
2.2. Core neural network architectures for rehabilitation 26
2.3. Applications of neural networks in rehabilitation timeline optimization 30
2.4. Neural network-based rehabilitation timeline optimization problems 34
2.5. Conclusions of neural network-enhanced rehabilitation timeline optimization 36
2.6. Conclusion 38
2.7. References 39

Chapter 3. AI and Digital Twins for Personalized Physiotherapy Simulations 43
Chandrakant PATIL and Swapna KAMBLE

3.1. Introduction 44
3.2. Digital twin technology in healthcare 45
3.3. AI foundations for physiotherapy simulations 47
3.4. Integration of AI and digital twins for personalized physiotherapy 49
3.5. Applications in musculoskeletal rehabilitation 51
3.6. Applications in neurological rehabilitation 52
3.7. Real-time monitoring and predictive analytics 54
3.8. Challenges and ethical considerations 55
3.9. Future directions 56
3.10. Conclusion 58
3.11. References 59

Chapter 4. ML for Outcome Prediction in Orthopedic Physiotherapy 61
Poonam PATIL and Jiwan DEHANKAR

4.1. Introduction 62
4.2. ML in healthcare and physiotherapy 63
4.3. Data sources for outcome prediction in orthopedic physiotherapy 65
4.4. ML algorithms for outcome prediction 68
4.5. Applications in orthopedic physiotherapy 70
4.6. Challenges and limitations 74
4.7. Future directions and clinical implications 75
4.8. Conclusion 76
4.9. References 77

Chapter 5. Computer-Vision-based Fall-Risk Assessment in Physiotherapy Patients 81
T. Poovishnu DEVI and Chandrayani ROKDE

5.1. Introduction 82
5.2. Methodologies for computer-vision-based fall-risk assessment 83
5.3. Applications of computer-vision-based fall-risk assessment in physiotherapy 87
5.4. Challenges and limitations 89
5.5. Future directions and research opportunities 91
5.6. Conclusion 94
5.7. References 95

Chapter 6. AI-Enhanced Virtual Reality Environments for Immersive Physiotherapy 99
S. ANANDH and P. BAINALWAR

6.1. Introduction 100
6.2. Foundations of AI and VR in physiotherapy 101
6.3. Immersive virtual environments for rehabilitation 103
6.4. AI algorithms for personalized physiotherapy 105
6.7. Clinical evidence and case studies 110
6.8. Challenges, ethical issues and limitations 111
6.9. Future directions in AI-enhanced VR physiotherapy 113
6.10. Conclusion 115
6.11. References 116

Chapter 7. Predictive Modeling of Muscle Recovery Using DL 119
Suraj KANASE and Kalpana MALPE

7.1. Introduction 120
7.2. Physiological basis of muscle recovery 121
7.3. Traditional approaches to prediction 123
7.4. DL techniques for predictive modeling 124
7.5. Data sources and modalities 127
7.6. Model architectures and frameworks 130
7.7. Clinical applications and case studies 132
7.8. Challenges and limitations 134
7.9. Conclusion 136
7.10. References 136

Chapter 8. AI and Cloud-Based Platforms for Remote Physiotherapy Supervision 141
Sandeep SHINDE and Shamla MANTRI

8.1. Introduction 142
8.2. AI in remote physiotherapy supervision 143
8.3. Cloud-based platforms for telerehabilitation 146
8.4. Synergistic integration of AI and cloud technologies 148
8.5. Clinical applications and case studies 153
8.6. Benefits and opportunities 155
8.7. Challenges and limitations 157
8.8. Future directions 159
8.9. Conclusion 161
8.10. References 162

Chapter 9. ML Algorithms for Movement Quality Scoring in Physiotherapy Sessions 165
Vaishali JAGTAP and G.M. VAIDYA

9.1. Introduction 166
9.2. Data acquisition methods for movement analysis 167
9.3. Feature extraction and preprocessing 170
9.4. ML algorithms for MQS 172
9.5. Applications in physiotherapy sessions 175
9.6. Challenges and limitations 178
9.7. Future directions 180
9.8. Conclusion 181
9.9. References 182

Chapter 10. AI-Powered Rehabilitation Robotics for Assisted Physiotherapy 185
Mandar MALAWADE and Fazil SHEIKH

10.1. Introduction 186
10.2. Overview of rehabilitation robotics 187
10.3. AI in rehabilitation robotics 189
10.4. AI techniques for assisted physiotherapy 190
10.5. Applications in neurological and musculoskeletal rehabilitation 194
10.6. Human–robot interaction and patient engagement 195
10.7. IoMT and wearable integration 197
10.8. Challenges and limitations 199
10.9. Future directions 200
10.10. Conclusion 201
10.11. References 201

Chapter 11. Therapeutic Approaches in Cerebral Palsy 205
Mandar MALAWADE and G. VARADHARAJULU

11.1. Introduction 206
11.2. Therapeutic approaches in cerebral palsy 208
11.3. Neurodevelopmental therapy (NDT) 209
11.4. Sensory integration (SI) 210
11.5. Play therapy 211
11.6. Combining NDT with sensory integration or play therapy 212
11.7. Conclusion 213
11.8. References 214

Chapter 12. Knowledge, Attitude and Practice of Breast Self-Examination Among Women in the Era of AI-Driven Innovations in Physiotherapy and Oncology 217
Ankita DURGAWALE, Vaishali JAGTAP, Trupti YADAV and Rujuta NENE

12.1. Introduction 218
12.2. Breast self-examination: concept, importance and current recommendations 220
12.3. Knowledge of breast self-examination among women 222
12.4. Attitude toward breast self-examination 223
12.5. Practice of breast self-examination 224
12.6. Role of AI in breast cancer screening and early detection 224
12.7. AI-driven innovations in physiotherapy for breast cancer care 225
12.8. Integrating AI with breast self-examination education and practice 226
12.9. Conclusion 228
12.10. References 228

List of Authors 233
Index 237