AI-driven Innovations in Physiotherapy and Oncology 4

Abhishek Kumar is an assistant director and professor in the Department of Computer Science and Engineering at Chandigarh University, Mohali, India. His expertise spans AI, renewable energy and image processing.Priya Batta is an associate professor at Amity School of Engineering and Technology, Amity University Punjab, Mohali, India. Her research specializes in AI, blockchain and the IoT.Sachin Ahuja is the Executive Director of Engineering and a professor at Chandigarh University, Mohali, India. His research specializes in AI, machine learning and data mining.Pramod Singh Rathoreis an assistant professor at Manipal University Jaipur, India. His research interests include NS2, networks, data mining and DBMS.

• Preface xviiAbhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHOREIntroduction xixAbhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHOREChapter 1. ML for Predictive Recovery Models in Physiotherapy 1Suraj KANASE and G.M. VAIDYA1.1. Introduction 11.2. Overview of ML in healthcare 31.3. ML techniques for predictive recovery in physiotherapy 41.4. Applications of ML in physiotherapy 71.5. Integration of ML with digital health tools 101.6. Challenges in implementing ML predictive models 121.7. Future directions 141.8. Conclusion 161.9. References 16Chapter 2. AI-Powered Gait Analysis for Post-Injury Rehabilitation 21Pragati SALUNKHE and K. GAVHALE2.1. Introduction 212.2. Fundamentals of gait analysis 232.3. Conventional methods and their limitations 252.4. AI techniques in gait analysis 272.5. Wearable and vision-based systems 302.6. Applications in post-injury rehabilitation 322.7. Clinical integration and telerehabilitation 342.8. Challenges and future directions 362.9. Conclusion 372.10. References 38Chapter 3. DL Applications in Automated Physiotherapy Progress Tracking 41Ankita DURGAWALE and Fazil SHEIKH3.1. Introduction 423.2. Foundations of DL in healthcare 433.3. Physiotherapy and the need for automated progress tracking 453.4. Computer vision approaches for physiotherapy monitoring 473.5. wearable sensor integration and DL models 493.6. Multimodal data fusion for comprehensive progress tracking 503.7. Personalization and adaptive DL in physiotherapy 533.8. Telerehabilitation and remote monitoring applications 543.9. Challenges in clinical integration 563.10. Conclusion 573.11. References 58Chapter 4. Real-Time AI Feedback Systems for Postural Correction in Physiotherapy 61Smita PATIL and Rasika Ranjit CHAFLE4.1. Introduction 624.2. The role of posture in physiotherapy 624.3. Evolution of AI in physiotherapy 644.4. Real-time AI feedback systems: mechanisms and technologies 664.5. Applications in postural correction 694.6. Wearables, sensors and vision-based AI approaches 714.7. Benefits in clinical and home-based physiotherapy 724.8. Challenges and limitations 744.9. Future directions 764.10. Conclusion 774.11. References 78Chapter 5. Wearable Sensor Data and ML for Remote Physiotherapy Monitoring 81Sandeep SHINDE and Shamla MANTRI5.1. Introduction 825.2. Wearable sensors for remote physiotherapy monitoring 835.3. ML for sensor data interpretation in remote physiotherapy 875.4. Applications and case studies of wearable sensor data and ML in remote physiotherapy 905.5. Challenges and future directions 935.6. Conclusion 965.7. References 96Chapter 6. AI-Driven Decision Support Systems for Personalized Physiotherapy Plans 101Shraddha MOHITE and P. BAINALWAR6.1. Introduction 1016.2. Foundations of AI-DSS 1036.3. Applications of AI-DSS in personalized physiotherapy 1066.4. Clinical benefits and patient outcomes 1096.5. Challenges and limitations 1116.6. Future directions 1136.7. Conclusion 1166.8. References 116Chapter 7. Reinforcement Learning for Adaptive Exercise Prescription in Rehabilitation 121Omkar SOMADE and Chandrayani ROKDE7.1. Introduction 1227.2. RL foundations for rehabilitation. 1247.3. Robotic rehabilitation applications 1277.4. Beyond robotics: chronic disease and treatment regimes 1297.5. Sensor integration and data-driven RL 1317.6. Neuromechanical adaptations, ethics, challenges and future directions 1347.7. Conclusion 1367.8. References 136Chapter 8. Computer Vision and AI for Automated Joint Range-of-Motion Assessment 141T. Poovishnu DEVI and Jiwan DEHANKAR8.1. Introduction 1418.2. Traditional approaches to ROM assessment 1448.3. CV-based ROM assessment 1478.4. Clinical applications of CV and AI for ROM assessment 1498.5. Challenges and ethical concerns in automated ROM assessment 1518.6. Future directions in AI and CV for ROM assessment 1548.7. Conclusion 1578.8. References 157Chapter 9. ML Models for Early Detection of Movement Disorders in Physiotherapy 161S. ANANDH and Swapna KAMBLE9.1. Introduction 1629.2. Overview of movement disorders in physiotherapy 1639.3. ML approaches in movement disorder detection 1669.4. Data sources and feature extraction in physiotherapy 1689.5. Clinical applications in physiotherapy 1729.6. Challenges, limitations and ethical considerations 1759.7. Conclusion 1779.8. References 178Chapter 10. AI-Based Predictive Analytics for Sports Injury Prevention and Recovery 181Vaishali JAGTAP and Kalpana MALPE10.1. Introduction 18210.2. Data sources for predictive analytics in sports 18310.3. AI techniques in sports injury prediction 18610.4. Applications in injury prevention 18810.5. Applications in injury recovery and rehabilitation 19010.6. Ethical, practical and implementation challenges 19210.7. Future directions 19510.8. Conclusion 19710.9. References 197List of Authors 201Index 205