Abhishek Kumar, Senior IEEE Member and Professor at Chandigarh University, India, is a prolific researcher with 170+ publications and has international postdoctoral experience. His expertise spans AI, renewable energy and image processing.Priya Batta is Associate Professor at Amity School of Engineering and Technology, Amity University Punjab, Mohali, India. She has over 12 years of academic experience and has edited several books. She actively contributes her research to reputed journals and conferences. Her expertise includes AI, blockchain and IoT.Sachin Ahuja is Executive Director of Engineering and Professor at Chandigarh University, India. He has guided numerous ME and PhD scholars, and currently specializes in AI, machine learning and data mining.Pramod Singh Rathore, Assistant Professor at Manipal University Jaipur, India, has over 12 years of experience and 85+ publications. His research interests include NS2, networks, data mining, DBMS and professional memberships, including ACM and IAENG.

• Preface xxiiiAbhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHOREChapter 1. Reinforcement Learning Models for Adaptive Cancer Rehabilitation in Physiotherapy 1Trupti YADAV and Sanjay BADJATE1.1. Introduction 21.2. Fundamentals of reinforcement learning (RL) 41.3. Cancer rehabilitation needs and challenges 71.4. RL-based framework for adaptive rehabilitation 81.5. Benefits of RL in cancer physiotherapy 111.6. Limitations and ethical considerations 131.7. Future directions 141.8. Conclusion 151.9. References 15Chapter 2. AI-Enabled Gait and Balance Assessment in Oncology Rehabilitation 19Dhairyasheel PATIL and Pankaj THOTE2.1. Introduction 192.2. Clinical background: gait and balance in cancer survivors 212.3. AI-enabled gait and balance assessment: overview of technologies 242.4. Evidence base and validation in the oncology setting 262.5. Core components of an AI-enabled oncology gait–balance platform 262.6. Implementation in oncology settings: use cases and workflow 282.7. Challenges and considerations 292.8. Future directions and research needs 302.9. Conclusion 312.10. References 32Chapter 3. Deep Learning-Driven Fatigue Monitoring in Cancer Physiotherapy Programs 35Anand GUDUR and Faisal Hussain HUSSAIN3.1. Introduction 353.2. Cancer-related fatigue and physiotherapy 373.3. Traditional and sensor-based monitoring 383.4. Deep learning models for fatigue monitoring 403.5. Integration into cancer physiotherapy 413.6. Applications and use cases 423.7. Strengths, limitations and challenges 443.8. Future directions 453.9. Conclusion 463.10. References 47Chapter 4. Predictive Modeling of Lymphedema Risk Using AI in Oncology Physiotherapy 51Rashmi GUDUR and Mrudula NIMBARTE4.1. Introduction 524.2. Clinical background: lymphedema in oncology 544.3. Rationale for predictive modeling 554.4. AI and ML overview 564.5. Model development approaches 584.6. Performance metrics and model comparisons 604.7. Explainability and clinical integration 624.8. Role in oncology physiotherapy 634.9. Challenges and limitations 634.10. Future directions 644.11. Conclusion 654.12. References 65Chapter 5. AI-Based Movement Quality Scoring for Post-Chemotherapy Rehabilitation 69Trupti YADAV and Rahul PETHE5.1. Introduction 695.2. Impact of chemotherapy on physical function 715.3. AI technologies for movement quality assessment 735.4. Clinical applications in post-chemotherapy rehabilitation 785.5. Challenges and limitations 805.6. Future directions and opportunities 825.7. Conclusion 835.8. References 83Chapter 6. Virtual Reality and AI for Pain Management in Cancer Physiotherapy 87Dhairyasheel PATIL and Abhay KASHETWAR6.1. Introduction 886.2. Cancer pain: scope and challenges 906.3. VR in pain management 916.4. AI in pain management 946.5. Integrating VR and AI: a synergistic approach 966.6. Case studies and clinical implementations 976.7. Technical and ethical considerations 986.8. Future directions 996.9. Conclusion 1006.10. References 101Chapter 7. Machine Learning for Optimizing Exercise Intensity in Oncology Rehabilitation 105Anand GUDUR and Himanshu WAGH7.1. Introduction 1057.2. Exercise intensity in oncology rehabilitation 1077.3. Machine learning in healthcare and rehabilitation 1097.4. ML techniques for exercise intensity optimization 1117.5. Data sources for ML modeling 1147.6. Challenges and limitations 1167.7. Future directions 1187.8. Conclusion 1197.9. References 119Chapter 8. AI-Driven Digital Twins for Simulating Physiotherapy Outcomes in Cancer Care 123Rashmi GUDUR and Mrudula NIMBARTE8.1. Introduction 1238.2. Background and theoretical framework 1258.3. Current research landscape 1288.4. Framework for AI-driven DT in cancer physiotherapy 1298.5. Use cases and scenario examples 1328.6. Evidence of effectiveness 1338.7. Ethical, practical and regulatory challenges 1338.8. Future directions and research agenda 1358.9. Conclusion 1368.10. References 137Chapter 9. Natural Language Processing of Patient Feedback to Personalize Oncology Physiotherapy 141Trupti YADAV and Faisal Hussain HUSSAIN9.1. Introduction 1429.2. Sources of patient feedback in oncology physiotherapy 1439.3. NLP techniques applied to patient feedback 1459.4. Personalizing oncology physiotherapy using NLP insights 1489.5. Case studies and systems in practice 1509.6. Challenges and limitations 1519.7. Future directions 1529.8. Ethical considerations 1549.9. Conclusion 1549.10. References 155Chapter 10. AI-Enhanced Biomechanical Feedback Systems for Radiation Therapy Recovery 159Dhairyasheel PATIL and Himanshu WAGH10.1. Introduction 15910.2. Radiation therapy sequelae and rehabilitation needs 16110.3. Biomechanical feedback technologies in RT recovery 16410.4. Role of AI in biomechanical feedback systems 16710.5. Examples and case studies of integrated systems 16910.6. Benefits of AI-enhanced biomechanical feedback for RT recovery 17010.7. Challenges and limitations 17010.8. Future directions 17110.9. Conclusion 17210.10. References 173Chapter 11. Computer Vision for Real-time Postural Correction in Cancer Physiotherapy 177Anand GUDUR and Rahul PETHE11.1. Introduction 17811.2. CV technologies for postural correction 18011.3. Applications in cancer physiotherapy 18311.4. Validation and clinical studies 18411.5. Challenges and barriers 18611.6. Innovations and integrations 18811.7. Future directions 19011.8. Conclusion 19111.9. References 191Chapter 12. AI-Driven Remote Physiotherapy Platforms for Immunocompromised Cancer Patients 195Rashmi GUDUR and Abhay KASHETWAR12.1. Introduction 19612.2. Background: cancer rehabilitation needs and barriers 19712.3. Telerehabilitation in oncology 19812.4. Enabling technologies in AI-driven remote physiotherapy 19912.5. Commercial platforms and use cases 20112.6. Benefits for immunocompromised cancer patients 20212.7. Challenges and risks 20412.8. Ethical, regulatory and implementation considerations 20612.9. Future directions and research agenda 20712.10. Conclusion 20812.11. References 209Chapter 13. Machine Learning for Early Detection of Mobility Decline in Oncology Patients 213Trupti YADAV and Sanjay BADJATE13.1. Introduction 21413.2. Clinical context: mobility decline in oncology 21513.3. ML approaches 21713.4. Predictive models for mobility decline 21913.5. Applications in oncology 22213.6. Technological platforms 22313.7. Challenges and limitations 22413.8. Future directions 22513.9. Conclusion 22713.10. References 227Chapter 14. Predictive Analytics for Return-to-Function Timelines in Cancer Survivors 231Dhairyasheel PATIL and Pankaj THOTE14.1. Introduction 23114.2. Scope and definitions 23314.3. Current evidence on functional recovery in cancer survivors 23514.4. Predictive analytics and machine learning approaches 23714.5. Integrating patient-generated and wearable data 24114.6. Broader AI and predictive techniques in oncology 24414.7. Future directions in predictive analytics for cancer survivorship 24614.8. Conclusion 24814.9. References 248Chapter 15. AI-Enabled Monitoring of Neuromuscular Recovery in Cancer Rehabilitation 253Rashmi GUDUR and Abhay KASHETWAR15.1. Introduction 25315.2. Neuromuscular impairments in cancer survivors 25515.3. AI technologies for neuromuscular monitoring 25615.4. Clinical applications in cancer rehabilitation 26115.5. Challenges and considerations 26315.6. Future research directions 26615.7. Conclusion 26915.8. References 269Chapter 16. Automated Motion Capture Systems for Oncology Physiotherapy Using AI 273Anand GUDUR and Mrudula NIMBARTE16.1. Introduction 27416.2. Overview of motion capture technologies in physiotherapy 27516.3. AI techniques in motion data processing 28016.4. Clinical applications in oncology rehabilitation 28416.5. Validation, performance metrics and outcomes 28716.6. Challenges and ethical considerations 28916.7. Future directions and research opportunities 29116.8. Conclusion 29216.9. References 293Chapter 17. Machine Learning to Forecast Rehabilitation Needs After Oncological Surgery 297Trupti YADAV and Abhay KASHETWAR17.1. Introduction 29817.2. Rehabilitation needs after oncological surgery 29917.3. ML in healthcare 30217.4. Data sources for forecasting rehabilitation needs 30317.5. ML models for predicting rehabilitation needs 30517.6. Applications in oncology rehabilitation forecasting 30817.7. Model validation and evaluation 30917.8. Future directions and research opportunities 31117.9. Conclusion 31217.10. References 312Chapter 18. AI-Driven Wearable Sensors for Personalized Cancer Recovery Programs 317Dhairyasheel PATIL and Sanjay BADJATE18.1. Introduction 31718.2. Summary of wearable sensors 31918.3. AI integration: from data to insight 32018.4. Uses in cancer recovery 32318.5. System architectures and practical implementations 32618.6. Benefits and impact 33118.7. Challenges and future directions 33318.8. Conclusion 33618.9. References 336Chapter 19. Computer Vision-Based Range of Motion Analysis in Oncology Physiotherapy 339Anand GUDUR and Pankaj THOTE19.1. Introduction 33919.2. CV techniques in ROM analysis 34119.3. Oncology physiotherapy: unique needs and challenges 34419.4. Proposed framework for oncology CV-based ROM analysis 34919.5. Benefits and opportunities 35119.6. Limitations and challenges 35319.7. Future directions and research opportunities 35519.8. Conclusion 35719.9. References 358Chapter 20. AI-Powered Robotic Assistance for Cancer Patient Physiotherapy 361Rashmi GUDUR and Faisal Hussain HUSSAIN20.1. Introduction 36120.2. Background and clinical context 36220.3. AI-enabled robotic rehabilitation technologies 36420.4. Oncology-specific applications 36620.5. Advantages and benefits 36920.6. Implementation challenges 37120.7. Future directions 37320.8. Conclusion 37520.9. References 376List of Authors 379Index 381

Abhishek Kumar, Senior IEEE Member and Professor at Chandigarh University, India, is a prolific researcher with 170+ publications and has international postdoctoral experience. His expertise spans AI, renewable energy and image processing.Priya Batta is Associate Professor at Amity School of Engineering and Technology, Amity University Punjab, Mohali, India. She has over 12 years of academic experience and has edited several books. She actively contributes her research to reputed journals and conferences. Her expertise includes AI, blockchain and IoT.Sachin Ahuja is Executive Director of Engineering and Professor at Chandigarh University, India. He has guided numerous ME and PhD scholars, and currently specializes in AI, machine learning and data mining.Pramod Singh Rathore, Assistant Professor at Manipal University Jaipur, India, has over 12 years of experience and 85+ publications. His research interests include NS2, networks, data mining, DBMS and professional memberships, including ACM and IAENG.

• Preface xxiiiAbhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHOREChapter 1. Reinforcement Learning Models for Adaptive Cancer Rehabilitation in Physiotherapy 1Trupti YADAV and Sanjay BADJATE1.1. Introduction 21.2. Fundamentals of reinforcement learning (RL) 41.3. Cancer rehabilitation needs and challenges 71.4. RL-based framework for adaptive rehabilitation 81.5. Benefits of RL in cancer physiotherapy 111.6. Limitations and ethical considerations 131.7. Future directions 141.8. Conclusion 151.9. References 15Chapter 2. AI-Enabled Gait and Balance Assessment in Oncology Rehabilitation 19Dhairyasheel PATIL and Pankaj THOTE2.1. Introduction 192.2. Clinical background: gait and balance in cancer survivors 212.3. AI-enabled gait and balance assessment: overview of technologies 242.4. Evidence base and validation in the oncology setting 262.5. Core components of an AI-enabled oncology gait–balance platform 262.6. Implementation in oncology settings: use cases and workflow 282.7. Challenges and considerations 292.8. Future directions and research needs 302.9. Conclusion 312.10. References 32Chapter 3. Deep Learning-Driven Fatigue Monitoring in Cancer Physiotherapy Programs 35Anand GUDUR and Faisal Hussain HUSSAIN3.1. Introduction 353.2. Cancer-related fatigue and physiotherapy 373.3. Traditional and sensor-based monitoring 383.4. Deep learning models for fatigue monitoring 403.5. Integration into cancer physiotherapy 413.6. Applications and use cases 423.7. Strengths, limitations and challenges 443.8. Future directions 453.9. Conclusion 463.10. References 47Chapter 4. Predictive Modeling of Lymphedema Risk Using AI in Oncology Physiotherapy 51Rashmi GUDUR and Mrudula NIMBARTE4.1. Introduction 524.2. Clinical background: lymphedema in oncology 544.3. Rationale for predictive modeling 554.4. AI and ML overview 564.5. Model development approaches 584.6. Performance metrics and model comparisons 604.7. Explainability and clinical integration 624.8. Role in oncology physiotherapy 634.9. Challenges and limitations 634.10. Future directions 644.11. Conclusion 654.12. References 65Chapter 5. AI-Based Movement Quality Scoring for Post-Chemotherapy Rehabilitation 69Trupti YADAV and Rahul PETHE5.1. Introduction 695.2. Impact of chemotherapy on physical function 715.3. AI technologies for movement quality assessment 735.4. Clinical applications in post-chemotherapy rehabilitation 785.5. Challenges and limitations 805.6. Future directions and opportunities 825.7. Conclusion 835.8. References 83Chapter 6. Virtual Reality and AI for Pain Management in Cancer Physiotherapy 87Dhairyasheel PATIL and Abhay KASHETWAR6.1. Introduction 886.2. Cancer pain: scope and challenges 906.3. VR in pain management 916.4. AI in pain management 946.5. Integrating VR and AI: a synergistic approach 966.6. Case studies and clinical implementations 976.7. Technical and ethical considerations 986.8. Future directions 996.9. Conclusion 1006.10. References 101Chapter 7. Machine Learning for Optimizing Exercise Intensity in Oncology Rehabilitation 105Anand GUDUR and Himanshu WAGH7.1. Introduction 1057.2. Exercise intensity in oncology rehabilitation 1077.3. Machine learning in healthcare and rehabilitation 1097.4. ML techniques for exercise intensity optimization 1117.5. Data sources for ML modeling 1147.6. Challenges and limitations 1167.7. Future directions 1187.8. Conclusion 1197.9. References 119Chapter 8. AI-Driven Digital Twins for Simulating Physiotherapy Outcomes in Cancer Care 123Rashmi GUDUR and Mrudula NIMBARTE8.1. Introduction 1238.2. Background and theoretical framework 1258.3. Current research landscape 1288.4. Framework for AI-driven DT in cancer physiotherapy 1298.5. Use cases and scenario examples 1328.6. Evidence of effectiveness 1338.7. Ethical, practical and regulatory challenges 1338.8. Future directions and research agenda 1358.9. Conclusion 1368.10. References 137Chapter 9. Natural Language Processing of Patient Feedback to Personalize Oncology Physiotherapy 141Trupti YADAV and Faisal Hussain HUSSAIN9.1. Introduction 1429.2. Sources of patient feedback in oncology physiotherapy 1439.3. NLP techniques applied to patient feedback 1459.4. Personalizing oncology physiotherapy using NLP insights 1489.5. Case studies and systems in practice 1509.6. Challenges and limitations 1519.7. Future directions 1529.8. Ethical considerations 1549.9. Conclusion 1549.10. References 155Chapter 10. AI-Enhanced Biomechanical Feedback Systems for Radiation Therapy Recovery 159Dhairyasheel PATIL and Himanshu WAGH10.1. Introduction 15910.2. Radiation therapy sequelae and rehabilitation needs 16110.3. Biomechanical feedback technologies in RT recovery 16410.4. Role of AI in biomechanical feedback systems 16710.5. Examples and case studies of integrated systems 16910.6. Benefits of AI-enhanced biomechanical feedback for RT recovery 17010.7. Challenges and limitations 17010.8. Future directions 17110.9. Conclusion 17210.10. References 173Chapter 11. Computer Vision for Real-time Postural Correction in Cancer Physiotherapy 177Anand GUDUR and Rahul PETHE11.1. Introduction 17811.2. CV technologies for postural correction 18011.3. Applications in cancer physiotherapy 18311.4. Validation and clinical studies 18411.5. Challenges and barriers 18611.6. Innovations and integrations 18811.7. Future directions 19011.8. Conclusion 19111.9. References 191Chapter 12. AI-Driven Remote Physiotherapy Platforms for Immunocompromised Cancer Patients 195Rashmi GUDUR and Abhay KASHETWAR12.1. Introduction 19612.2. Background: cancer rehabilitation needs and barriers 19712.3. Telerehabilitation in oncology 19812.4. Enabling technologies in AI-driven remote physiotherapy 19912.5. Commercial platforms and use cases 20112.6. Benefits for immunocompromised cancer patients 20212.7. Challenges and risks 20412.8. Ethical, regulatory and implementation considerations 20612.9. Future directions and research agenda 20712.10. Conclusion 20812.11. References 209Chapter 13. Machine Learning for Early Detection of Mobility Decline in Oncology Patients 213Trupti YADAV and Sanjay BADJATE13.1. Introduction 21413.2. Clinical context: mobility decline in oncology 21513.3. ML approaches 21713.4. Predictive models for mobility decline 21913.5. Applications in oncology 22213.6. Technological platforms 22313.7. Challenges and limitations 22413.8. Future directions 22513.9. Conclusion 22713.10. References 227Chapter 14. Predictive Analytics for Return-to-Function Timelines in Cancer Survivors 231Dhairyasheel PATIL and Pankaj THOTE14.1. Introduction 23114.2. Scope and definitions 23314.3. Current evidence on functional recovery in cancer survivors 23514.4. Predictive analytics and machine learning approaches 23714.5. Integrating patient-generated and wearable data 24114.6. Broader AI and predictive techniques in oncology 24414.7. Future directions in predictive analytics for cancer survivorship 24614.8. Conclusion 24814.9. References 248Chapter 15. AI-Enabled Monitoring of Neuromuscular Recovery in Cancer Rehabilitation 253Rashmi GUDUR and Abhay KASHETWAR15.1. Introduction 25315.2. Neuromuscular impairments in cancer survivors 25515.3. AI technologies for neuromuscular monitoring 25615.4. Clinical applications in cancer rehabilitation 26115.5. Challenges and considerations 26315.6. Future research directions 26615.7. Conclusion 26915.8. References 269Chapter 16. Automated Motion Capture Systems for Oncology Physiotherapy Using AI 273Anand GUDUR and Mrudula NIMBARTE16.1. Introduction 27416.2. Overview of motion capture technologies in physiotherapy 27516.3. AI techniques in motion data processing 28016.4. Clinical applications in oncology rehabilitation 28416.5. Validation, performance metrics and outcomes 28716.6. Challenges and ethical considerations 28916.7. Future directions and research opportunities 29116.8. Conclusion 29216.9. References 293Chapter 17. Machine Learning to Forecast Rehabilitation Needs After Oncological Surgery 297Trupti YADAV and Abhay KASHETWAR17.1. Introduction 29817.2. Rehabilitation needs after oncological surgery 29917.3. ML in healthcare 30217.4. Data sources for forecasting rehabilitation needs 30317.5. ML models for predicting rehabilitation needs 30517.6. Applications in oncology rehabilitation forecasting 30817.7. Model validation and evaluation 30917.8. Future directions and research opportunities 31117.9. Conclusion 31217.10. References 312Chapter 18. AI-Driven Wearable Sensors for Personalized Cancer Recovery Programs 317Dhairyasheel PATIL and Sanjay BADJATE18.1. Introduction 31718.2. Summary of wearable sensors 31918.3. AI integration: from data to insight 32018.4. Uses in cancer recovery 32318.5. System architectures and practical implementations 32618.6. Benefits and impact 33118.7. Challenges and future directions 33318.8. Conclusion 33618.9. References 336Chapter 19. Computer Vision-Based Range of Motion Analysis in Oncology Physiotherapy 339Anand GUDUR and Pankaj THOTE19.1. Introduction 33919.2. CV techniques in ROM analysis 34119.3. Oncology physiotherapy: unique needs and challenges 34419.4. Proposed framework for oncology CV-based ROM analysis 34919.5. Benefits and opportunities 35119.6. Limitations and challenges 35319.7. Future directions and research opportunities 35519.8. Conclusion 35719.9. References 358Chapter 20. AI-Powered Robotic Assistance for Cancer Patient Physiotherapy 361Rashmi GUDUR and Faisal Hussain HUSSAIN20.1. Introduction 36120.2. Background and clinical context 36220.3. AI-enabled robotic rehabilitation technologies 36420.4. Oncology-specific applications 36620.5. Advantages and benefits 36920.6. Implementation challenges 37120.7. Future directions 37320.8. Conclusion 37520.9. References 376List of Authors 379Index 381

Abhishek Kumar, Senior IEEE Member and Professor at Chandigarh University, India, is a prolific researcher with 170+ publications and has international postdoctoral experience. His expertise spans AI, renewable energy and image processing.Priya Batta is Associate Professor at Amity School of Engineering and Technology, Amity University Punjab, Mohali, India. She has over 12 years of academic experience and has edited several books. She actively contributes her research to reputed journals and conferences. Her expertise includes AI, blockchain and IoT.Sachin Ahuja is Executive Director of Engineering and Professor at Chandigarh University, India. He has guided numerous ME and PhD scholars, and currently specializes in AI, machine learning and data mining.Pramod Singh Rathore, Assistant Professor at Manipal University Jaipur, India, has over 12 years of experience and 85+ publications. His research interests include NS2, networks, data mining, DBMS and professional memberships, including ACM and IAENG.

• Preface xxiiiAbhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHOREChapter 1. Reinforcement Learning Models for Adaptive Cancer Rehabilitation in Physiotherapy 1Trupti YADAV and Sanjay BADJATE1.1. Introduction 21.2. Fundamentals of reinforcement learning (RL) 41.3. Cancer rehabilitation needs and challenges 71.4. RL-based framework for adaptive rehabilitation 81.5. Benefits of RL in cancer physiotherapy 111.6. Limitations and ethical considerations 131.7. Future directions 141.8. Conclusion 151.9. References 15Chapter 2. AI-Enabled Gait and Balance Assessment in Oncology Rehabilitation 19Dhairyasheel PATIL and Pankaj THOTE2.1. Introduction 192.2. Clinical background: gait and balance in cancer survivors 212.3. AI-enabled gait and balance assessment: overview of technologies 242.4. Evidence base and validation in the oncology setting 262.5. Core components of an AI-enabled oncology gait–balance platform 262.6. Implementation in oncology settings: use cases and workflow 282.7. Challenges and considerations 292.8. Future directions and research needs 302.9. Conclusion 312.10. References 32Chapter 3. Deep Learning-Driven Fatigue Monitoring in Cancer Physiotherapy Programs 35Anand GUDUR and Faisal Hussain HUSSAIN3.1. Introduction 353.2. Cancer-related fatigue and physiotherapy 373.3. Traditional and sensor-based monitoring 383.4. Deep learning models for fatigue monitoring 403.5. Integration into cancer physiotherapy 413.6. Applications and use cases 423.7. Strengths, limitations and challenges 443.8. Future directions 453.9. Conclusion 463.10. References 47Chapter 4. Predictive Modeling of Lymphedema Risk Using AI in Oncology Physiotherapy 51Rashmi GUDUR and Mrudula NIMBARTE4.1. Introduction 524.2. Clinical background: lymphedema in oncology 544.3. Rationale for predictive modeling 554.4. AI and ML overview 564.5. Model development approaches 584.6. Performance metrics and model comparisons 604.7. Explainability and clinical integration 624.8. Role in oncology physiotherapy 634.9. Challenges and limitations 634.10. Future directions 644.11. Conclusion 654.12. References 65Chapter 5. AI-Based Movement Quality Scoring for Post-Chemotherapy Rehabilitation 69Trupti YADAV and Rahul PETHE5.1. Introduction 695.2. Impact of chemotherapy on physical function 715.3. AI technologies for movement quality assessment 735.4. Clinical applications in post-chemotherapy rehabilitation 785.5. Challenges and limitations 805.6. Future directions and opportunities 825.7. Conclusion 835.8. References 83Chapter 6. Virtual Reality and AI for Pain Management in Cancer Physiotherapy 87Dhairyasheel PATIL and Abhay KASHETWAR6.1. Introduction 886.2. Cancer pain: scope and challenges 906.3. VR in pain management 916.4. AI in pain management 946.5. Integrating VR and AI: a synergistic approach 966.6. Case studies and clinical implementations 976.7. Technical and ethical considerations 986.8. Future directions 996.9. Conclusion 1006.10. References 101Chapter 7. Machine Learning for Optimizing Exercise Intensity in Oncology Rehabilitation 105Anand GUDUR and Himanshu WAGH7.1. Introduction 1057.2. Exercise intensity in oncology rehabilitation 1077.3. Machine learning in healthcare and rehabilitation 1097.4. ML techniques for exercise intensity optimization 1117.5. Data sources for ML modeling 1147.6. Challenges and limitations 1167.7. Future directions 1187.8. Conclusion 1197.9. References 119Chapter 8. AI-Driven Digital Twins for Simulating Physiotherapy Outcomes in Cancer Care 123Rashmi GUDUR and Mrudula NIMBARTE8.1. Introduction 1238.2. Background and theoretical framework 1258.3. Current research landscape 1288.4. Framework for AI-driven DT in cancer physiotherapy 1298.5. Use cases and scenario examples 1328.6. Evidence of effectiveness 1338.7. Ethical, practical and regulatory challenges 1338.8. Future directions and research agenda 1358.9. Conclusion 1368.10. References 137Chapter 9. Natural Language Processing of Patient Feedback to Personalize Oncology Physiotherapy 141Trupti YADAV and Faisal Hussain HUSSAIN9.1. Introduction 1429.2. Sources of patient feedback in oncology physiotherapy 1439.3. NLP techniques applied to patient feedback 1459.4. Personalizing oncology physiotherapy using NLP insights 1489.5. Case studies and systems in practice 1509.6. Challenges and limitations 1519.7. Future directions 1529.8. Ethical considerations 1549.9. Conclusion 1549.10. References 155Chapter 10. AI-Enhanced Biomechanical Feedback Systems for Radiation Therapy Recovery 159Dhairyasheel PATIL and Himanshu WAGH10.1. Introduction 15910.2. Radiation therapy sequelae and rehabilitation needs 16110.3. Biomechanical feedback technologies in RT recovery 16410.4. Role of AI in biomechanical feedback systems 16710.5. Examples and case studies of integrated systems 16910.6. Benefits of AI-enhanced biomechanical feedback for RT recovery 17010.7. Challenges and limitations 17010.8. Future directions 17110.9. Conclusion 17210.10. References 173Chapter 11. Computer Vision for Real-time Postural Correction in Cancer Physiotherapy 177Anand GUDUR and Rahul PETHE11.1. Introduction 17811.2. CV technologies for postural correction 18011.3. Applications in cancer physiotherapy 18311.4. Validation and clinical studies 18411.5. Challenges and barriers 18611.6. Innovations and integrations 18811.7. Future directions 19011.8. Conclusion 19111.9. References 191Chapter 12. AI-Driven Remote Physiotherapy Platforms for Immunocompromised Cancer Patients 195Rashmi GUDUR and Abhay KASHETWAR12.1. Introduction 19612.2. Background: cancer rehabilitation needs and barriers 19712.3. Telerehabilitation in oncology 19812.4. Enabling technologies in AI-driven remote physiotherapy 19912.5. Commercial platforms and use cases 20112.6. Benefits for immunocompromised cancer patients 20212.7. Challenges and risks 20412.8. Ethical, regulatory and implementation considerations 20612.9. Future directions and research agenda 20712.10. Conclusion 20812.11. References 209Chapter 13. Machine Learning for Early Detection of Mobility Decline in Oncology Patients 213Trupti YADAV and Sanjay BADJATE13.1. Introduction 21413.2. Clinical context: mobility decline in oncology 21513.3. ML approaches 21713.4. Predictive models for mobility decline 21913.5. Applications in oncology 22213.6. Technological platforms 22313.7. Challenges and limitations 22413.8. Future directions 22513.9. Conclusion 22713.10. References 227Chapter 14. Predictive Analytics for Return-to-Function Timelines in Cancer Survivors 231Dhairyasheel PATIL and Pankaj THOTE14.1. Introduction 23114.2. Scope and definitions 23314.3. Current evidence on functional recovery in cancer survivors 23514.4. Predictive analytics and machine learning approaches 23714.5. Integrating patient-generated and wearable data 24114.6. Broader AI and predictive techniques in oncology 24414.7. Future directions in predictive analytics for cancer survivorship 24614.8. Conclusion 24814.9. References 248Chapter 15. AI-Enabled Monitoring of Neuromuscular Recovery in Cancer Rehabilitation 253Rashmi GUDUR and Abhay KASHETWAR15.1. Introduction 25315.2. Neuromuscular impairments in cancer survivors 25515.3. AI technologies for neuromuscular monitoring 25615.4. Clinical applications in cancer rehabilitation 26115.5. Challenges and considerations 26315.6. Future research directions 26615.7. Conclusion 26915.8. References 269Chapter 16. Automated Motion Capture Systems for Oncology Physiotherapy Using AI 273Anand GUDUR and Mrudula NIMBARTE16.1. Introduction 27416.2. Overview of motion capture technologies in physiotherapy 27516.3. AI techniques in motion data processing 28016.4. Clinical applications in oncology rehabilitation 28416.5. Validation, performance metrics and outcomes 28716.6. Challenges and ethical considerations 28916.7. Future directions and research opportunities 29116.8. Conclusion 29216.9. References 293Chapter 17. Machine Learning to Forecast Rehabilitation Needs After Oncological Surgery 297Trupti YADAV and Abhay KASHETWAR17.1. Introduction 29817.2. Rehabilitation needs after oncological surgery 29917.3. ML in healthcare 30217.4. Data sources for forecasting rehabilitation needs 30317.5. ML models for predicting rehabilitation needs 30517.6. Applications in oncology rehabilitation forecasting 30817.7. Model validation and evaluation 30917.8. Future directions and research opportunities 31117.9. Conclusion 31217.10. References 312Chapter 18. AI-Driven Wearable Sensors for Personalized Cancer Recovery Programs 317Dhairyasheel PATIL and Sanjay BADJATE18.1. Introduction 31718.2. Summary of wearable sensors 31918.3. AI integration: from data to insight 32018.4. Uses in cancer recovery 32318.5. System architectures and practical implementations 32618.6. Benefits and impact 33118.7. Challenges and future directions 33318.8. Conclusion 33618.9. References 336Chapter 19. Computer Vision-Based Range of Motion Analysis in Oncology Physiotherapy 339Anand GUDUR and Pankaj THOTE19.1. Introduction 33919.2. CV techniques in ROM analysis 34119.3. Oncology physiotherapy: unique needs and challenges 34419.4. Proposed framework for oncology CV-based ROM analysis 34919.5. Benefits and opportunities 35119.6. Limitations and challenges 35319.7. Future directions and research opportunities 35519.8. Conclusion 35719.9. References 358Chapter 20. AI-Powered Robotic Assistance for Cancer Patient Physiotherapy 361Rashmi GUDUR and Faisal Hussain HUSSAIN20.1. Introduction 36120.2. Background and clinical context 36220.3. AI-enabled robotic rehabilitation technologies 36420.4. Oncology-specific applications 36620.5. Advantages and benefits 36920.6. Implementation challenges 37120.7. Future directions 37320.8. Conclusion 37520.9. References 376List of Authors 379Index 381