AI-driven Innovations in Physiotherapy and Oncology 3

Name: AI-driven Innovations in Physiotherapy and Oncology 3
Brand: Wiley-ISTE
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Abhishek Kumar Priya Batta Sachin Ahuja Pramod Singh Rathore(Editor)

Wiley-ISTE (Publisher)

1st Edition

Published on 1. April 2026

432 pages

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Content

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1. Reinforcement Learning Models for Adaptive Cancer Rehabilitation in Physiotherapy
1.1. Introduction
1.2. Fundamentals of reinforcement learning (RL)
1.2.1. Overview
1.2.2. Key components in cancer rehabilitation
1.3. Cancer rehabilitation needs and challenges
1.3.1. Functional deficits post-cancer treatment
1.3.2. Challenges in current rehabilitation protocols
1.4. RL-based framework for adaptive rehabilitation
1.4.1. System architecture
1.4.2. Data sources
1.4.3. Adaptive learning process
1.5. Benefits of RL in cancer physiotherapy
1.6. Limitations and ethical considerations
1.6.1. Privacy and security issues
1.6.2. Data bias
1.6.3. Interpretability
1.6.4. Dependency and dehumanization
1.7. Future directions
1.7.1. Multi-agent reinforcement learning (RL)
1.7.2. Transfer learning
1.7.3. Human-in-the-loop approaches
1.7.4. Integration with electronic health records (EHRs)
1.8. Conclusion
1.9. References
Chapter 2. AI-Enabled Gait and Balance Assessment in Oncology Rehabilitation
2.1. Introduction
2.2. Clinical background: gait and balance in cancer survivors
2.3. AI-enabled gait and balance assessment: overview of technologies
2.3.1. Markerless computer vision/pose estimation
2.3.2. Wearable IMUs and smart sensing systems
2.3.3. Telehealth and mobile-based systems
2.3.4. Learnable and privacy-protective AI
2.3.5. VR and rehabilitation robotics are transforming
2.4. Evidence base and validation in the oncology setting
2.5. Core components of an AI-enabled oncology gaitâ?"balance platform
2.5.1. Non-intrusive and accessible data capture
2.5.2. AI models and analytics
2.5.3. Dashboard and reporting interface
2.5.4. Integration with rehabilitation prescription
2.6. Implementation in oncology settings: use cases and workflow
2.6.1. Baseline and ongoing assessment
2.6.2. Personalized intervention and progress monitoring
2.6.3. Fall risk stratification and safety monitoring
2.6.4. Remote telerehabilitation
2.7. Challenges and considerations
2.7.1. Data quality and clinical validation
2.7.2. Bias and generalizability
2.7.3. Regulatory and integration hurdles
2.7.4. Patient engagement and equity.
2.8. Future directions and research needs
2.9. Conclusion
2.10. References
Chapter 3. Deep Learning-Driven Fatigue Monitoring in Cancer Physiotherapy Programs
3.1. Introduction
3.2. Cancer-related fatigue and physiotherapy
3.3. Traditional and sensor-based monitoring
3.4. Deep learning models for fatigue monitoring
3.4.1. Forecasting symptom escalation
3.4.2. Sensor-based and multimodal fatigue
3.4.3. Assessing rehabilitation exercise
3.5. Integration into cancer physiotherapy
3.6. Applications and use cases
3.6.1. Real-time fatigue monitoring
3.6.2. Predictive warnings
3.6.3. Personalized therapy
3.7. Strengths, limitations and challenges
3.7.1. Strengths of DL-driven fatigue monitoring systems
3.7.2. Limitations and gaps in the current research
3.7.3. Ethical and operational challenges
3.8. Future directions
3.9. Conclusion
3.10. References
Chapter 4. Predictive Modeling of Lymphedema Risk Using AI in Oncology Physiotherapy
4.1. Introduction
4.2. Clinical background: lymphedema in oncology
4.3. Rationale for predictive modeling
4.4. AI and ML overview
4.5. Model development approaches
4.5.1. Data collection
4.5.2. Feature engineering and selection
4.5.3. Model training and validation
4.6. Performance metrics and model comparisons
4.7. Explainability and clinical integration
4.8. Role in oncology physiotherapy
4.9. Challenges and limitations
4.10. Future directions
4.11. Conclusion
4.12. References
Chapter 5. AI-Based Movement Quality Scoring for Post-Chemotherapy Rehabilitation
5.1. Introduction
5.2. Impact of chemotherapy on physical function
5.2.1. Common side effects affecting mobility
5.2.2. Limitations of traditional movement assessment
5.2.3. Need for advanced assessment tools
5.3. AI technologies for movement quality assessment
5.3.1. Motion capture systems
5.3.2. Pose estimation and computer vision
5.3.3. Machine learning and deep learning models
5.3.4. Scoring metrics and interpretability
5.3.5. Real-time feedback systems
5.4. Clinical applications in post-chemotherapy rehabilitation
5.4.1. Gait analysis
5.4.2. Upper limb function assessment
5.4.3. Balance and fall risk detection
5.4.4. Remote rehabilitation and telehealth
5.5. Challenges and limitations
5.5.1. Data limitations
5.5.2. Interpatient variability
5.5.3. Sensor and environment constraints
5.5.4. Privacy and ethical issues
5.5.5. Clinical integration
5.6. Future directions and opportunities
5.6.1. Individualized rehabilitation programs
5.6.2. Multi-modal data fusion
5.6.3. Federated learning and data sharing
5.6.4. Gamification and engagement
5.6.5. Cross-domain application
5.7. Conclusion
5.8. References
Chapter 6. Virtual Reality and AI for Pain Management in Cancer Physiotherapy
6.1. Introduction
6.2. Cancer pain: scope and challenges
6.2.1. Prevalence and impact
6.2.2. Traditional physiotherapy weakness
6.3. VR in pain management
6.3.1. What VR?
6.3.2. Mechanisms of VR for pain relief
6.3.3. Clinical applications of VR in cancer physiotherapy
6.3.4. Evidence-based observations
6.4. AI in pain management
6.4.1. Role of AI in healthcare
6.4.2. AI in physiotherapy
6.4.3. AI-driven personalization
6.5. Integrating VR and AI: a synergistic approach
6.5.1. Real-time adaptive VR systems
6.5.2. Closed-loop feedback systems
6.5.3. Gamification and behavioral reinforcement
6.6. Case studies and clinical implementations
6.6.1. Use case I
6.6.2. Use case II
6.6.3. Pediatric cancer and VR distraction therapy
6.7. Technical and ethical considerations
6.7.1. Data privacy and consent
6.7.2. Bias in AI algorithms
6.7.3. Technology access and digital divide
6.8. Future directions
6.8.1. Multimodal integration
6.8.2. Remote and home-based rehabilitation
6.8.3. Prediction of chronic pain
6.8.4. AI sources and virtual therapists
6.9. Conclusion
6.10. References
Chapter 7. Machine Learning for Optimizing Exercise Intensity in Oncology Rehabilitation
7.1. Introduction
7.2. Exercise intensity in oncology rehabilitation
7.2.1. Defining exercise intensity
7.2.2. Challenges in modulating intensity
7.3. Machine learning in healthcare and rehabilitation
7.3.1. Machine learning overview
7.3.2. Machine learning (ML) in broader rehabilitation contexts
7.4. ML techniques for exercise intensity optimization
7.4.1. Supervised learning for exercise intensity prediction
7.4.2. Unsupervised learning and clustering for patient stratification
7.4.3. Reinforcement learning (RL) for adaptive exercise prescription
7.5. Data sources for ML modeling
7.5.1. Wearable devices and biosensors
7.5.2. Patient-reported outcomes (PROs)
7.5.3. Electronic health records (EHRs)
7.6. Challenges and limitations
7.6.1. Data quality and heterogeneity
7.6.2. Model interpretability.
7.6.3. Ethical and legal issues
7.6.4. Clinical validation
7.7. Future directions
7.7.1. Multimodal data fusion
7.7.2. Customizable digital twin
7.7.3. Federated learning
7.7.4. Integration with genomic data
7.8. Conclusion
7.9. References
Chapter 8. AI-Driven Digital Twins for Simulating Physiotherapy Outcomes in Cancer Care
8.1. Introduction
8.2. Background and theoretical framework
8.2.1. Digital twins in healthcare
8.2.2. Musculoskeletal and physiotherapy digital twins
8.2.3. Virtual physiological human (VPH) and multiscale modeling
8.2.4. The role of artificial intelligence
8.3. Current research landscape
8.3.1. Bibliometric and thematic mapping
8.3.2. Oncology-focused DT research
8.3.3. Technology prototypes in rehabilitation
8.4. Framework for AI-driven DT in cancer physiotherapy
8.4.1. Data modalities and integration
8.4.2. Modeling and simulation architecture
8.4.3. Simulation of physiotherapy interventions
8.5. Use cases and scenario examples
8.5.1. Post-mastectomy shoulder rehabilitation
8.5.2. Cancer-related fatigue and gait training
8.5.3. Movement optimization and pain management
8.5.4. Telerehabilitation scaling
8.6. Evidence of effectiveness
8.6.1. Outcome results
8.6.2. Oncology DT impact
8.6.3. Analogous systems in MSK physiotherapy
8.7. Ethical, practical and regulatory challenges
8.7.1. Data privacy and sharing
8.7.2. Model explainability and clinician trust
8.7.3. Equity and accessibility
8.7.4. The computational and resource challenges represent
8.7.5. Legal liability
8.8. Future directions and research agenda
8.8.1. Clinical trials
8.8.2. Multi-mode data integration
8.8.3. Advanced AI methods
8.8.4. AR/VR and gamification
8.8.5. Regulatory frameworks
8.8.6. Interdisciplinary teamwork
8.9. Conclusion
8.10. References
Chapter 9. Natural Language Processing of Patient Feedback to Personalize Oncology Physiotherapy
9.1. Introduction
9.2. Sources of patient feedback in oncology physiotherapy
9.2.1. Clinical notes and consult transcripts
9.2.2. Patient-reported outcome measures (PROMs) with free-text fields
9.2.3. Online health forums and support groups
9.2.4. Satisfaction surveys and feedback forms
9.2.5. Social media and digital health apps
9.3. NLP techniques applied to patient feedback
9.3.1. Preprocessing of unstructured feedback
9.3.2. Sentiment analysis
9.3.3. Topic modeling
9.3.4. Named entity recognition (NER)
9.3.5. Text classification
9.4. Personalizing oncology physiotherapy using NLP insights
9.4.1. Flexible therapy scheduling
9.4.2. Flexible exercise program design
9.4.3. Monitoring adherence and engagement
9.4.4. Psychosocial support integration
9.4.5. Decision support capabilities
9.5. Case studies and systems in practice
9.5.1. IBM Watson Oncology
9.5.2. Patient voice
9.5.3. CAIRN (Computer-Aided Instruction in Rehabilitation Needs) prototype
9.6. Challenges and limitations
9.6.1. Uncertainty and draftsmanship in words
9.6.2. Lack of data and labeling
9.6.3. Lack of generalizability
9.6.4. Workflow integration
9.6.5. Ethical and regulatory issues
9.7. Future directions
9.7.1. Development of domain-specific NLP models
9.7.2. Multilingual and cross-cultural adaptation
9.7.3. Integration with multimodal data
9.7.4. Explainable NLP models
9.7.5. Real-time and longitudinal analysis
9.8. Ethical considerations
9.9. Conclusion
9.10. References
Chapter 10. AI-Enhanced Biomechanical Feedback Systems for Radiation Therapy Recovery
10.1. Introduction
10.2. Radiation therapy sequelae and rehabilitation needs
10.3. Biomechanical feedback technologies in RT recovery
10.3.1. Wearable sensors and electromyography (EMG)
10.3.2. Surface imaging and optical tracking
10.3.3. Biomechanical modeling and finite element analysis
10.4. Role of AI in biomechanical feedback systems
10.4.1. AI in motion prediction and tracking
10.4.2. Data fusion and adaptive feedback
10.4.3. AI-driven robotic and wearable exoskeleton systems
10.4.4. Augmented and virtual reality feedback
10.5. Examples and case studies of integrated systems
10.6. Benefits of AI-enhanced biomechanical feedback for RT recovery
10.7. Challenges and limitations
10.8. Future directions
10.9. Conclusion
10.10. References
Chapter 11. Computer Vision for Real-time Postural Correction in Cancer Physiotherapy
11.1. Introduction
11.1.1. Background
11.1.2. Rise of CV in physiotherapy
11.1.3. Relevance in oncology rehabilitation
11.1.4. Aim of the chapter
11.2. CV technologies for postural correction
11.2.1. Marker-based motion capture systems
11.2.2. Markerless vision systems
11.2.3. Integration with AR and visual feedback
11.2.4. Real-time processing and edge computing
11.3. Applications in cancer physiotherapy
11.3.1. Breast cancer rehabilitation
11.3.2. Head and neck cancer
11.3.3. Pelvic and colorectal cancer
11.3.4. Cancer-related fatigue and sarcopenia
11.4. Validation and clinical studies
11.4.1. Accuracy and reliability
11.4.2. User acceptance
11.4.3. Limitations in clinical trials
11.5. Challenges and barriers
11.5.1. Technical limitations
11.5.2. Clinical validation
11.5.3. Privacy and ethics
11.5.4. Equity and accessibility
11.6. Innovations and integrations
11.6.1. Integration with digital twins
11.6.2. Sensor fusion
11.6.3. Gamification and AR/VR
11.6.4. Personalized AI coaching
11.7. Future directions
11.7.1. Clinical trials
11.7.2. Model improvement
11.7.3. Accessibility initiatives
11.7.4. Regulatory guidelines
11.8. Conclusion
11.9. References
Chapter 12. AI-Driven Remote Physiotherapy Platforms for Immunocompromised Cancer Patients
12.1. Introduction
12.2. Background: cancer rehabilitation needs and barriers
12.2.1. Physical sequelae of cancer treatment
12.2.2. Immunocompromised and access barriers
12.3. Telerehabilitation in oncology
12.3.1. Evidence of feasibility and efficacy
12.3.2. Limitations of conventional telerehabilitation
12.4. Enabling technologies in AI-driven remote physiotherapy
12.4.1. Computer vision and motion tracking
12.4.2. Machine learning (ML) for personalization
12.4.3. AI-driven virtual assistants and coaching
12.4.4. Remote patient monitoring and multimodal data integration
12.4.5. Federated learning and privacy-preserving AI
12.5. Commercial platforms and use cases
12.6. Benefits for immunocompromised cancer patients
12.6.1. Clinical safety and reduced infection risk
12.6.2. Personalized rehabilitation and feedback
12.6.3. Improved access and adherence
12.6.4. Data-driven outcome tracking
12.6.5. Scalability and therapist leveraging
12.7. Challenges and risks
12.7.1. Technology access and digital literacy
12.7.2. Data privacy and security
12.7.3. Algorithm transparency and trust
12.7.4. Clinical appropriateness and oversight
12.7.5. Equity, bias and cultural adaptation
12.8. Ethical, regulatory and implementation considerations
12.8.1. Informed consent and autonomy
12.8.2. Regulatory compliance
12.8.3. Interoperability and integration.
12.8.4. Pricing and reimbursement model
12.9. Future directions and research agenda
12.9.1. Clinical trials comparison of AI-augmented and traditional physiotherapy
12.9.2. Explainable AI to transparent decision-making
12.9.3. Federated learning for advanced secured institutional cooperation
12.9.4. Multimodal data integration for personalized AI-enhanced rehabilitation
12.9.5. Cultural and linguistic adaptation to acceptable use
12.9.6. Long-term evaluation of benefits and cost effects
12.9.7. Ethics and human control
12.10. Conclusion
12.11. References
Chapter 13. Machine Learning for Early Detection of Mobility Decline in Oncology Patients
13.1. Introduction
13.2. Clinical context: mobility decline in oncology
13.2.1. Etiology and prevalence
13.2.2. Impact on outcomes
13.2.3. Current assessment modalities
13.3. ML approaches
13.3.1. ML paradigms
13.3.2. Data modalities
13.3.3. Feature engineering
13.4. Predictive models for mobility decline
13.4.1. Model types and performance metrics
13.4.2. Modeling pipelines
13.4.3. Representative studies (hypothetical examples)
13.5. Applications in oncology
13.5.1. Chemotherapy-induced decline
13.5.2. The patient reports no pain or discomfort in their post-surgical period
13.5.3. Radiation and combined modality therapy
13.5.4. Hematologic malignancies and bone marrow transplantation
13.6. Technological platforms
13.6.1. Wearable sensors
13.6.2. Smartphones and consumer devices
13.6.3. Telehealth integration
13.6.4. Edge versus cloud analytics
13.7. Challenges and limitations
13.7.1. Data quality and heterogeneity
13.7.2. Sample size and labeling
13.7.3. Model interpretability
13.7.4. Clinical integration
13.7.5. Ethical, privacy and regulatory concerns
13.8. Future directions
13.8.1. Multimodal data fusion
13.8.2. Individualized predictive models
13.8.3. Adaptive and real-time analytics
13.8.4. Federated learning
13.8.5. Implementation research and clinical trials
13.8.6. Regulatory pathways and ethical design
13.9. Conclusion
13.10. References
Chapter 14. Predictive Analytics for Return-to-Function Timelines in Cancer Survivors
14.1. Introduction
14.2. Scope and definitions
14.2.1. Dimensions of function in cancer survivorship
14.2.2. Timeline definitions: acute versus long-term recovery
14.2.3. Data sources for function assessment
14.2.4. Population-specific considerations
14.3. Current evidence on functional recovery in cancer survivors
14.3.1. Physical function recovery
14.3.2. Cognitive and neuropsychological recovery
14.3.3. Return to work and societal reintegration
14.3.4. Functional decline and comorbidities
14.4. Predictive analytics and machine learning approaches
14.4.1. Traditional statistical approaches
14.4.2. Emergence of ML in RTF prediction
14.4.3. Time-to-event and survival ML models
14.4.4. Interpretable ML for clinical use
14.4.5. Data challenges and feature engineering
14.5. Integrating patient-generated and wearable data
14.5.1. What constitutes PGHD?
14.5.2. Role of wearable devices in function monitoring
14.5.3. Predictive modeling with wearable data
14.5.4. Mobile health platforms and engagement tools
14.5.5. Challenges and considerations
14.6. Broader AI and predictive techniques in oncology
14.6.1. Prognosis and survival prediction models
14.6.2. Length of stay and readmission prediction
14.6.3. Radiomics and imaging-based predictions
14.6.4. Knowledge-informed ML
14.6.5. Real-world applications and decision support systems
14.7. Future directions in predictive analytics for cancer survivorship
14.7.1. Multimodal data integration
14.7.2. Real-time and adaptive prediction models
14.7.3. Federated and privacy-preserving learning
14.7.4. Explainable and human-centered AI
14.8. Conclusion
14.9. References
Chapter 15. AI-Enabled Monitoring of Neuromuscular Recovery in Cancer Rehabilitation
15.1. Introduction
15.2. Neuromuscular impairments in cancer survivors
15.3. AI technologies for neuromuscular monitoring
15.3.1. Wearable sensor-based systems
15.3.2. CV and pose estimation
15.3.3. Multimodal data fusion
15.3.4. Edge versus cloud processing
15.4. Clinical applications in cancer rehabilitation
15.4.1. Baseline quantification and personalized goal setting
15.4.2. Monitoring recovery and adapting rehabilitation
15.4.3. Remote telerehabilitation and home monitoring
15.4.4. Complication risk stratification
15.4.5. Outcomes research and quality improvement
15.5. Challenges and considerations
15.5.1. Data quality, labeling and generalizability
15.5.2. AI model understanding and clinicianâ?Ts confidence
15.5.3. Privacy, security and data sharing
15.5.4. Regulatory and reimbursement barriers
15.5.5. Integration into clinical workflow
15.5.6. Equity and access
15.6. Future research directions
15.6.1. Constructing large, various and longitudinal datasets
15.6.2. Transfer learning and self-supervised learning
15.6.3. Personalized and XAI systems
15.6.4. Clinical trials and effectiveness studies
15.6.5. Policy and reimbursement advocacy
15.6.6. Emerging technological innovations
15.7. Conclusion
15.8. References
Chapter 16. Automated Motion Capture Systems for Oncology Physiotherapy Using AI
16.1. Introduction
16.1.1. Background
16.1.2. Rise of motion capture in physiotherapy
16.1.3. Relevance in oncology
16.2. Overview of motion capture technologies in physiotherapy
16.2.1. Marker-based optical MoCap
16.2.2. Markerless capture systems
16.2.3. Wearable sensor systems
16.2.4. Hybrid systems
16.2.5. Smartphone-based capture
16.3. AI techniques in motion data processing
16.3.1. Preprocessing and feature extraction
16.3.2. Pose estimation and skeleton tracking
16.3.3. Gait analysis and classification
16.3.4. Activity recognition systems that automate recognition of activities
16.3.5. Anomaly detection and personalized modeling
16.3.6. Closed-loop systems and real-time feedback
16.3.7. Multimodal and multitask
16.4. Clinical applications in oncology rehabilitation
16.4.1. Monitoring chemotherapy
16.4.2. Evaluating recovery of functions post-cancer interventions
16.4.3. Managing and detecting lymphedema
16.4.4. Exercising and telerehabilitation
16.4.5. Fatigue and frailty assessment
16.5. Validation, performance metrics and outcomes
16.5.1. Technical validation of motion capture systems
16.5.2. Model validation and evaluation metrics
16.5.3. Clinical validation and utility
16.5.4. Usability and acceptance of patients
16.6. Challenges and ethical considerations
16.6.1. Data quality and variability
16.6.2. Small and diverse collections of datasets
16.6.3. Scientific transparency and reliance
16.7. Future directions and research opportunities
16.7.1. Custom tailored and dynamic models
16.7.2. Clinical implication
16.7.3. Real-time, on the edge computing and motion analysis
16.7.4. Federated and privacy-preserving learning
16.8. Conclusion
16.9. References
Chapter 17. Machine Learning to Forecast Rehabilitation Needs After Oncological Surgery
17.1. Introduction
17.2. Rehabilitation needs after oncological surgery
17.2.1. Types of rehabilitation
17.2.2. Needs for rehabilitation and its determinants
17.3. ML in healthcare
17.3.1. Definition and scope
17.3.2. Types of ML models
17.4. Data sources for forecasting rehabilitation needs
17.4.1. EHRs
17.4.2. Radiology data
17.4.3. Wearable sensors
17.4.4. Patient-reported outcome measures
17.4.5. Genomic and biomolecular data
17.5. ML models for predicting rehabilitation needs
17.5.1. Decision trees and random forests
17.5.2. SVMs
17.5.3. Logistic regression
17.5.4. Neural networks and deep learning
17.5.5. Gradient boosting machines (e.g. XGBoost, LightGBM, etc.)
17.6. Applications in oncology rehabilitation forecasting
17.6.1. Colorectal surgery
17.6.2. Breast cancer surgery
17.6.3. Head and neck cancer
17.6.4. Thoracic and abdominal cancer
17.7. Model validation and evaluation
17.7.1. Metrics
17.7.2. Validation methods
17.7.3. Challenges
17.8. Future directions and research opportunities
17.8.1. Personalized rehabilitation pathways
17.8.2. Longitudinal modeling
17.8.3. Federated learning
17.8.4. Fusion with robotics and virtual reality
17.8.5. Costâ?"benefit analysis
17.9. Conclusion
17.10. References
Chapter 18. AI-Driven Wearable Sensors for Personalized Cancer Recovery Programs
18.1. Introduction
18.2. Summary of wearable sensors
18.2.1. Inertial measurement units
18.2.2. Electromyography sensors
18.2.3. Pressure sensors and gait insoles
18.2.4. Physiological biosensors
18.2.5. Multisensor fusion platforms
18.3. AI integration: from data to insight
18.3.1. Predictive and real-time data processing analytics
18.3.2. Sensor fusion and multivariate analysis
18.3.3. Personalized anomaly detection systems
18.3.4. Adaptive feedback and timely interventions
18.3.5. Remote clinical integration and patient monitoring
18.4. Uses in cancer recovery
18.4.1. Monitoring activity and performance
18.4.2. Frailty and gait assessment
18.4.3. Assessment of recovery and muscle activation
18.4.4. Biochemical and inflammatory marker monitoring
18.4.5. Psychosocial and behavioral health monitoring
18.5. System architectures and practical implementations
18.5.1. Layered system architecture for cancer recovery
18.5.2. Conversational AI wearables
18.5.3. Smart garments and integrated sensor systems
18.5.4. Point-of-care AI diagnostic platforms
18.5.5. Cloud-integrated AI platforms and dashboards
18.6. Benefits and impact
18.6.1. Personalized and adaptive rehabilitation
18.6.2. Early detection and proactive interventions
18.6.3. Enhanced patient engagement and empowerment
18.6.4. Continuity of care and remote monitoring
18.6.5. Cost-effectiveness and healthcare efficiency
18.7. Challenges and future directions
18.7.1. Data quality, standardization and labeling
18.7.2. Bias, equity and generalizability
18.7.3. Interpretability and clinical trust
18.7.4. Regulatory and reimbursement pathways
18.7.5. Future innovations and research directions
18.8. Conclusion
18.9. References
Chapter 19. Computer Vision-Based Range of Motion Analysis in Oncology Physiotherapy
19.1. Introduction
19.2. CV techniques in ROM analysis
19.2.1. Markerless motion capture systems
19.2.2. RGB camera-based systems and consumer devices
19.2.3. Deep learning and ML integration
19.2.4. Real-time analysis and edge computing
19.3. Oncology physiotherapy: unique needs and challenges
19.3.1. Clinical context of ROM impairments in oncology
19.3.2. ROM assessment challenges specific to oncology
19.3.3. Integration challenges in oncology settings
19.3.4. Evidence from related clinical populations
19.4. Proposed framework for oncology CV-based ROM analysis
19.4.1. Hardware setup and environment
19.4.2. Pose estimation and skeletal modeling
19.4.3. ROM computation and kinematic analysis
19.5. Benefits and opportunities
19.5.1. Enhanced accuracy and objectivity in assessment
19.5.2. Remote monitoring and telehealth compatibility
19.5.3. Scalable and cost-effective care delivery
19.5.4. Personalized and data-driven rehabilitation
19.6. Limitations and challenges
19.6.1. Technical limitations of current CV systems
19.6.2. Patient-related factors in oncology
19.6.3. Clinical workflow integration
19.7. Future directions and research opportunities
19.7.1. Development of oncology-specific pose estimation models
19.7.2. Integration with telerehabilitation platforms
19.7.3. Personalized and longitudinal movement modeling
19.7.4. Multimodal sensor fusion
19.8. Conclusion
19.9. References
Chapter 20. AI-Powered Robotic Assistance for Cancer Patient Physiotherapy
20.1. Introduction
20.2. Background and clinical context
20.3. AI-enabled robotic rehabilitation technologies
20.3.1. Robotic systems in rehabilitation
20.3.2. Integration of AI
20.3.3. Wearable sensors and biometric monitoring
20.3.4. Virtual and augmented reality integration
20.4. Oncology-specific applications
20.4.1. Breast cancer
20.4.2. Head and neck cancer
20.4.3. Colorectal and prostate cancer
20.4.4. Neurological cancers
20.4.5. Pediatric oncology
20.5. Advantages and benefits
20.5.1. Personalization and adaptivity
20.5.2. Precision and consistency
20.5.3. Improved patient engagement and motivation
20.5.4. Accessibility and remote care
20.5.5. Staff support and clinical efficiency
20.6. Implementation challenges
20.6.1. High cost and limited accessibility
20.6.2. Complexity of device use and setup
20.6.3. Clinical validation and evidence gaps
20.6.4. Ethical and data privacy concerns
20.6.5. Patient acceptance and psychological barriers
20.7. Future directions
20.7.1. Multimodal data fusion for precision rehabilitation
20.7.2. Emotionally intelligent and socially aware robots
20.7.3. Brainâ?"computer interfaces (BCIs)
20.7.4. Expanded home-based and community rehabilitation
20.7.5. Integration with AR and VR
20.8. Conclusion
20.9. References
List of Authors
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AI-driven Innovations in Physiotherapy and Oncology 3

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