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Wearable Technologies for Digital Medicine

Advancement, Models, and Applications

Edited by Sushruta Mishra, Subhashish Das and S. Balamurugan
Series: Industry 5.0 Transformation Applications
Copyright: 2026   |   Expected Pub Date:2025/12/30
ISBN: 9781394271146  |  Hardcover  |  
404 pages

One Line Description
Equip yourself with the multidisciplinary expertise to lead the digital medicine revolution with this comprehensive guide to the cutting-edge intersection of wearable technology, data science, and clinical care.

Description
The rapid evolution of digital technology has transformed nearly every aspect of human life—from communication and entertainment to healthcare and medicine. Among these advancements, smart wearables have emerged as a vital element of modern digital medicine, redefining how we monitor, diagnose, and manage health conditions. This book explores the dynamic intersection of wearable technology and medical science, providing an in-depth understanding of how these devices are revolutionizing healthcare delivery, patient monitoring, and disease management. By bridging engineering, data science, and clinical applications, it serves as a comprehensive resource for researchers, healthcare professionals, technology developers, and students interested in digital medicine.
Readers will find the volume:
• Emphasizes the importance of wearable-enabled e-medicine to create a smart and intelligent healthcare network;
• Introduces advanced architectural frameworks of digital healthcare and clinical systems;
• Provides diversified applications and case studies on smart wearable-enabled personalized healthcare services;
• Explores futuristic enhancements and challenges of different technologies in the context of wearable computing supported by e-medicine;
• Highlights techniques and methodologies related to emergency healthcare and improved customized patient care.

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Author / Editor Details
Sushruta Mishra, PhD is an Assistant Professor in the School of Computer Engineering at the Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India, with more than 11 years of experience. He has published more than 130 research articles in reputed international journals and conferences. His research interests include image processing, machine learning, Internet of Things, and cognitive computing.

Subhasis Dash, PhD is an Assistant Professor in the School of Computer Engineering at the Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India, with more than 21 years of experience. He has ten publications to his credit in international journals and conferences. His research interests include wireless sensor networks, distributed computing, and operating systems.

S. Balamurugan, PhD is the Director of Intelligent Research Consultancy Services, Coimbatore, Tamil Nadu, India. He has published more than 60 books, 300 articles in national and international journals and conferences, and 200 patents. He is the series editor for several book series and serves in various editorial capacities for several international journals.

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Table of Contents
Preface
Acknowledgement
Part 1: Introduction to Wearables in Healthcare
1. Wearable Machine Intelligence Devices: A Comprehensive Analysis of Current Applications and Future Directions in Context to Healthcare
Rohit Raj Pradhan
1.1 Introduction
1.1.1 Artificial Intelligence (AI)
1.1.1.1 History of AI
1.1.2 Introduction to Wearable Devices
1.1.2.1 Brief Review of Wearables
1.1.2.2 Working Principles of AI-Based Wearable Devices
1.2 Artificial Intelligence Principles Used
1.2.1 Feature Extraction and Engineering Feature
1.2.2 Utilization of Machine Learning Algorithms in Wearable Technology
1.2.2.1 Implementing Supervised Learning Techniques in Wearable Devices
1.2.2.2 Utilizing Unsupervised Learning Techniques in Wearable Technology
1.2.2.3 Utilizing Semi-Supervised Learning Techniques in Wearable Technology
1.2.2.4 Making Use of Reinforcement Education in Wearable Technology
1.3 Major Contributions
1.4 Use Cases of AI-Based Wearable
1.5 Model Proposed
1.5.1 Model Description
1.5.2 Working
1.6 Challenges in the Society
1.7 Conclusion
References
2. Feasibility Scope of Artificial Intelligence in Advanced Sensory Wearables
Sanjana Panigrahi and Vaishnavi Rani
2.1 Introduction
2.2 Main Contribution
2.3 Related Works
2.4 Applications of AI-Based Wearables
2.4.1 Leveraging Wearables and AI for Safety and Location Tracking
2.4.2 Integration of Artificial Intelligence in Augmented Reality (AR) and Virtual Reality (VR)
2.4.3 AI-Infused Wearable Devices: Shaping Sports Performance and Training
2.5 Everyday Wearables
2.5.1 Hearable Devices
2.5.2 Smart Glasses
2.5.3 Smart Clothing
2.5.4 Smart Rings
2.5.5 Smart Contact Lenses
2.5.6 Fitness Trackers
2.6 Medical Wearables: An In-Depth Exploration
2.6.1 Smart Insulin Pumps
2.6.2 Smart Prosthetics
2.6.3 Wearable Pain Relief Devices
2.6.4 Wearable SpO2 Monitors
2.6.5 Wearable ECG/EKG Monitors
2.7 Current Challenges in Society
2.7.1 Consumer Perspective and Technology Acceptance
2.7.2 Wearables Integration Setting Importance
2.7.3 Different Factors Motivating the Adoption of Wearables
2.7.4 The Role of AI in Wearable Healthcare
2.7.5 The Future Convergence of Wearable Technology and Healthcare
2.8 Conclusion
References
Part 2: Applications of Smart Wearables in Clinical Domain
3 Addressing the Integration of Wearable Devices in Chronic Disease Management
Aman Arpit and Rithwik Kumar Sahu
3.1 Introduction
3.2 Role of These Wearable Devices in the Continuous Monitoring of Health
3.3 Integration of Artificial Intelligence and Machine Learning in This Sector
3.3.1 Advanced Data Processing and Analysis
3.3.2 Personal Measures and Care Strategies
3.3.3 Proactive Screening and Early Intervention
3.4 Patient Engagement and Involvement
3.5 Some Wearable Electronic Devices
3.5.1 Hand-Worn
3.5.2 Optical Signal Monitoring
3.6 Biochemistry Index Monitoring
3.7 Organ Function Assistance
3.7.1 Wearable Cardioverter Defibrillator for Heart Disease
3.7.2 Wearable Artificial Kidney for Chronic Kidney Disease
3.7.3 Wearable Rehabilitation Robots in Neurologic Injuries
3.7.4 Nano Fusion Fabric
3.8 Additional Advantages of These Wearable Devices
3.8.1 Remote Monitoring and Telemedicine
3.8.2 Data-Driven Insights
3.8.3 Reduced Healthcare Costs
3.8.4 Clinical Trials and Research
3.8.5 Patient-Centered Outcomes
3.8.6 Behavioral Changes and Lifestyle Modifications
3.8.7 Real-World Data for Regulatory Approvals
3.8.8 Global Health Monitoring
3.9 Challenges Faced by These Wearable Devices in Healthcare
3.9.1 Accuracy and Reliability
3.9.2 Data Privacy and Security
3.9.3 User Acceptance and Adherence
3.9.4 Technological Limitations
3.9.5 Data Overload and Interpretation
3.9.6 Standardization and Interoperability
3.9.7 Regulatory and Ethical Considerations
3.9.8 Digital Divide
3.9.9 Integration into Clinical Workflow
3.9.10 Long-Term Sustainability
3.10 Future of Wearable Devices in the Healthcare Sector
3.11 Conclusion
References
4. From Sensors to Sentiments: An NLP-Powered Exploration of Clinical Data
Riya Singh, Tanmay Pandey and Fariya Afrin
4.1 Introduction
4.2 Objective of Study
4.3 Literature Review
4.3.1 Overview of Clinical Data Analysis
4.3.2 Role of NLP in Healthcare
4.3.3 The Latest Applications of NLP in Healthcare
4.3.3.1 Speech Recognition (SR) Technology
4.3.3.2 Sentiment Analysis, Opinion Mining, or Emotion AI
4.3.3.3 Medical Question–Answering System (MEANS)
4.3.3.4 Automatic Clinical Text Classification
4.3.3.5 Optical Character Recognition
4.3.4 The Use of Sensors in Healthcare
4.3.4.1 Types of Sensors Used in Clinical Data Collection
4.3.4.2 Applications of Sensor Data in Healthcare
4.3.5 Previous Research on Integrating NLP and Sensor Data in Clinical Analysis
4.4 Materials and Methods
4.4.1 Dataset
4.4.2 Proposed Framework
4.4.2.1 Data Pre-Processing
4.4.2.2 Feature Extraction
4.4.2.3 Classification
4.5 Experiments and Results
4.6 Benefits and Limitations
4.7 Conclusion
References
5. Emergence of Social Media Analysis in Personalized Healthcare
Pranav Kumar and Anchal Sahoo
5.1 Introduction
5.1.1 Personalized Healthcare
5.1.1.1 Key Components of Personalized Healthcare
5.1.1.2 Benefits of Personalized Healthcare
5.1.2 Social Media Analysis
5.1.3 Role of Social Media Analysis in Healthcare
5.1.3.1 Various Components and Functions
5.2 Main Contribution
5.2.1 Enhanced Healthcare Delivery
5.2.2 Improved Disease Surveillance
5.2.3 Increased Vaccination Uptake
5.2.4 Empowering Patients
5.2.5 Informed Policy Making
5.2.6 Reduced Health Disparities
5.2.7 Enhanced Public Health Communication
5.2.8 Advancement of Interdisciplinary Research
5.3 Literature Review
5.4 Case Study I
5.4.1 Leveraging Social Media Analysis for Influenza Outbreak Monitoring
5.5 Case Study II
5.5.1 Addressing Vaccine Hesitancy Through Targeted Social Media Analysis and Intervention
5.6 Author’s Take from Both Case Studies
5.7 Challenges
5.7.1 Challenges of Social Media Analytics
5.7.1.1 Data High-Quality and Reliability
5.7.1.2 Privacy and Moral Troubles
5.7.1.3 Bias and Incorrect Information
5.7.1.4 Quantity and Speed
5.7.1.5 Cross-Platform Integration
5.7.2 Challenges in Personalized Healthcare
5.7.2.1 Data Integration and Interoperability
5.7.2.2 Complexity of Health Data
5.7.2.3 Law and Regulatory Structures
5.7.2.4 Application and Adoption in Clinical Practice
5.7.2.5 Financial and Resource Restrictions
5.7.3 Challenges in the Role of Social Media Analysis in Personalized Healthcare
5.7.3.1 Analyzed Privacy Preservation
5.7.3.2 Therapeutic Relevance and Validity
5.7.3.3 Patient Empowerment and Participation
5.7.3.4 Healthcare Disparities and Discrimination Prevention
5.7.3.5 Multidisciplinary Collaboration
5.8 Conclusion
References
6. Pragmatic Analysis of Brain Tumor Using Deep Learning Approach
Sunil Kumar Mohapatra
6.1 Introduction
6.2 Related Work
6.3 Proposed Method
6.4 Analysis of the Proposed Framework
6.5 Pros and Cons of the Proposed Method
6.6 Conclusion
References
7. Recent Advancements in Generative Artificial Intelligence in Clinical Space
Oindrella Chatterjee
7.1 Introduction
7.1.1 Evolution of AI
7.1.2 Utilization of GAI in the Healthcare Sector
7.2 Main Contribution of the Paper
7.3 Literature Review
7.4 Proposed Model
7.4.1 Architecture of the Model
7.4.2 Comparison
7.5 Challenges of GAI in Healthcare
7.6 Constraints of Using GAI in Healthcare
7.7 Conclusion
References
8. Significance of Telemedicine in Improving Mental Health in Rural Areas
Biswojit Panda and Arup Ranjan Dalai
8.1 Introduction
8.2 Mental Health Care Disparities in Rural Areas
8.3 Telemedicine and Mental Health Care
8.4 Benefits of Telemedicine in Improving Access and Quality of Care
8.5 Challenges of Telemedicine Healthcare Services
8.6 Integration of Telepsychiatry in Medicare
8.7 Technology Infrastructure and Connectivity
8.8 Cultural and Linguistic Considerations
8.9 Patient Satisfaction, Acceptance, and Engagement
8.9.1 Strategies for Getting Patients On Board and Sticking with It
8.10 Collaborative Care Models in Telemedicine
8.10.1 Team-Based Approaches Mixing Primary Care and Mental Health
8.10.2 Teaming Up Local Providers with Telemedicine
8.11 Cost-Effectiveness and Financial Considerations
8.12 Ethical, Legal, and Privacy Considerations
8.12.1 Ethical Guidelines for Telemedicine in Mental Health Care
8.13 Legal and Regulatory Frameworks Governing Telemedicine Practices
8.14 Privacy, Confidentiality, and Data Protection in Telemedicine Services
8.15 Outcomes, Evaluation, and Long-Term Impact
8.16 Comparing Traditional In-Person Care to Telemedicine
8.17 Implications for Practice, Policy, and Future Directions
8.18 Conclusion
References
Part 3: Advanced Technologies of Smart Clinical Wearables
9. Automated Surgery: Innovations and Development of Robotics
Gargi Sansanwal, Rishabh Raj Srivastava and Bidisha Biswarupa Muduli
9.1 Introduction
9.1.1 History of Robotic Surgery
9.1.2 Characteristics of Robotic Surgery
9.1.3 Literature Review
9.1.4 Advantages of Robotic Surgery
9.1.5 Technical Constraints of Robotic Surgery
9.2 Proposed Model: The Da Vinci Surgical System
9.2.1 The Da Vinci System
9.3 Case Studies on Robotic Surgery
9.3.1 Robot-Assisted Liver Resection/Hepatectomy
9.3.1.1 Methods
9.3.1.2 Placement of the Port and Positioning of the Patient
9.3.1.3 Dissection of the Portal
9.3.1.4 Dissection of the Right Portal Pedicle Extraparenchymally (Right Hemi-Hepatectomy)
9.3.1.5 Dissection of the Left Portal Pedicle Extraparenchymally (Left Hemi-Hepatectomy)
9.3.1.6 Movement of the Right Liver (Right Hemi-Hepatectomy)
9.3.1.7 Movement of the Left Liver (Left Hemi-Hepatectomy)
9.3.1.8 Parenchymal Transection
9.3.1.9 Statistical Analysis
9.3.1.10 Results
9.3.2 Total Thyroidectomy Using Robots
9.3.2.1 Methods
9.3.2.2 Working Space Formation
9.3.2.3 Docking Stage
9.3.2.4 Console Stage
9.3.2.5 Results
9.3.2.6 Summary
9.4 Future Challenges of Robotic Surgery
9.5 Conclusion
References
10. Malware and e-Medicine: A Comparative Analysis and Risk Exploration
Pratyusa Mukherjee, Lopamudra Dalai and Shivansh Mani Tripathi
10.1 Introduction
10.1.1 e-Medicine
10.1.2 Malware
10.1.3 Cybersecurity in e-Medicine
10.2 Related Work
10.2.1 Fundamental Concepts
10.2.1.1 Pandas
10.2.1.2 Scikit-Learn
10.2.1.3 NumPy
10.2.1.4 LightGBM
10.2.1.5 Random Forest
10.2.1.6 Decision Tree
10.2.2 Literature Survey on Malware Detection
10.2.3 Literature Survey on Cyber-Risks in e-Medicine
10.3 Proposed Methodology
10.3.1 Data Preprocessing
10.3.2 Partitioning the Data
10.3.3 Training and Testing
10.3.4 Accuracy
10.4 Result
10.4.1 Method 1: Pandas and Random Forest
10.4.2 Method 2: Pandas and LightGBM
10.4.3 Method 3: Pandas and Decision Tree
10.4.4 Method 4: Scikit-Learn and Random Forest
10.4.5 Method 5: Scikit-Learn and LightGBM
10.4.6 Method 6: Scikit-Learn and Decision Tree
10.4.7 Method 7: NumPy and Random Forest
10.4.8 Method 8: NumPy and LightGBM
10.4.9 Method 9: NumPy and Decision Tree
10.4.10 Comparative Analysis
10.5 Conclusion and Future Work
References
11. Prediction of Mental Health Issues Among Working Professionals Using Intelligent Models and Ensemble Classifiers
Tridiv Swain
11.1 Introduction
11.2 Literature Review
11.3 Background Study About Mental Health
11.4 Methodology
11.5 Technology Employed in Current Times to Treat Mental Health Illnesses
11.6 System Architecture
11.7 Implementation
11.7.1 Data Cleaning
11.7.2 Data Analysis
11.7.3 Supervised Models
11.8 Machine Learning Models and Ensemble Classifiers
11.8.1 Logistic Regression
11.8.2 K-Neighbors Classification
11.8.3 Decision Tree Classification
11.8.4 C-Support Vector Classification
11.8.5 Bagging Meta-Estimation
11.8.6 AdaBoost
11.9 Result and Discussion
11.10 Impact on the Real World
11.11 Conclusion
References
12. Critical Representation of Smart and Distributed Modeling for Disease Risk Analysis
Gargi Sansanwal and Sanjolee Singh
12.1 Introduction
12.2 Hierarchical IoT Framework for Enhanced Health Surveillance
12.2.1 Tier 1: The Art of Data Harvesting
12.2.2 Tier 2: Secure Repositories of Health Data
12.2.3 Tier 3: Navigating Through Health Data Analytics
12.3 Pioneering IoT Networks for Instantaneous Disease Forecasting in Big Data Milieus
12.3.1 Architecting the Real-Time Health Prognosis System
12.3.1.1 The Genesis of Data: Diverse Health Data Sources
12.3.1.2 Kafka Streams: The Veins of Real-Time Data Influx
12.3.1.3 Spark’s Luminescence: Using Streaming Analytics to Illuminate Data
12.4 Unveiling the DBN Model: A Vanguard in Health Informatics
12.4.1 The Prelude: Data Gathering and Refinement
12.4.2 DBN Alchemy: Transmuting Data into Prognostic Insights
12.4.3 Figuring Out the DBN Puzzle: A Deep Dive into Its Heart
12.5 Related Works
12.6 Revolutionizing Disease Prognostics with IoT Innovations
12.6.1 Parkinson’s Disease
12.6.1.1 The Smartwatch: A New-Age Parkinson’s Monitor
12.6.1.2 Innovative Ink: A Novel Approach to Data Collection
12.6.1.3 Neurostimulator: The IoT Beacon of Hope for Parkinson’s
12.6.2 Cancer Detection: A New Frontier
12.6.2.1 Cancer’s IoT Observatory
12.6.2.2 Crafting the Prototype: Designing the Future of Cancer Care
12.6.3 Fighting COVID-19 Using IoT
12.6.3.1 Pandemic IoT Solutions
12.6.3.2 Monitor Breathing
12.6.3.3 Blood Oxygen Level Monitoring
12.6.3.4 Cold Chain Vaccine Monitoring
12.7 Conclusion
References
13. Predicting Autism: A Deep Learning Approach with CNN-LSTM
Soumyendu Das and Soham Patra
13.1 Introduction
13.2 Main Contribution
13.2.1 Introduction of ASD Framework
13.2.2 Adaptation of CNN-LSTM Schemes
13.2.3 Introduction of Federated Learning
13.2.4 Security and Privacy Concerns
13.2.5 Introduction of IoT Application
13.2.6 Related Works
13.2.7 Proposed Model
13.2.8 Diverse Datasets Integration
13.2.9 Federated Learning Framework
13.2.10 Multi-Modal Decision-Making
13.2.11 IoT ASD Applications and Vertical Federated Learning
13.2.12 Federated CNN-LSTM Framework for ASD Prediction
13.2.13 Enhanced CNN Offloading and Classification Strategies
13.2.14 Modified LSTM Scheme
13.3 Results and Analysis
13.4 Conclusion
13.4.1 Time of Complexity
References
14. Dominance of Ambience Intelligence in Clinical Zone
Lambodar Jena and Baidehi Jena
14.1 Introduction to Ambient Intelligence
14.2 Evolution of Ambient Intelligence in Healthcare
14.3 The Objective of the Work
14.4 Motivation of the Work
14.5 Supporting Infrastructure and Technology for Ambient Intelligence
14.5.1 Body Area Networks
14.5.2 Dense/Mesh Sensor Networks for Ambient-Assisted Living (AAL)
14.5.3 Sensor Technology
14.6 Dominant Methodologies in Ambient Intelligence
14.6.1 Activity Recognition
14.6.2 Behavioral Pattern Discovery
14.6.3 Anomaly Detection
14.6.4 Planning and Scheduling
14.6.5 Decision Support
14.6.6 Anonymization and Privacy-Preserving Techniques
14.7 Future Scope
14.8 Conclusion
References
Index

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