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Cyber Physical Human Systems

Emerging Trends and Applications
Edited by Anitha Velu, Prasanth Aruchamy, Raghu Ramamoorthy, Rajesh Kumar Dhanaraj, and Manjula Arunraj
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394402885 | Hardcover |
330 pages
Price: $225 USD

One Line Description
Discover how the seamless integration of human behavior modeling, digital twins, and deep neural networks is revolutionizing vital industries in this essential guide to the next generation of intelligent systems.

Description
Cyber-physical human systems are interconnected systems made of people, computers, and cyber-physical objects that can connect and disengage with one another in time and space. The implications of cyber-physical human systems are transforming our daily lives, workplaces, and social interactions. From manufacturing and industry to healthcare and government, these complex systems that integrate hardware, software, and networks are at the core of many vital sectors and applications. Maximizing effectiveness and raising productivity, they improve the interconnectivity of systems and devices across industries. This book comprehensively covers recent technological advancements and applications in the field of intelligent cyber-physical human systems, providing insights into emerging trends and applications in the context of new-generation intelligent systems. It explores advancements in cyber-physical human systems that integrate human behaviour modelling, Internet of Things, digital twins, industrial IoT, and deep neural networks for applications in 6G communication, robotics, healthcare, and autonomous vehicles. Using real-world case studies, this essential guide serves as a resource for academics and industry professionals alike.

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Author / Editor Details
Anitha Velu, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the Sri Sairam College of Engineering, Bangalore, India. She has published more than 15 papers in reputed journals and two patents. Her research interests include image processing, VLSI design, Internet of Things, quantum cryptography, ontology, and semantic web.

Prasanth Aruchamy, PhD is an Associate Professor in the Department of Computer Science and Engineering at the Vel Tech Rangarajan Dr. Sagunthala Research and Development Institute of Science and Technology, Chennai, India. He has published more than 45 research articles in international journals and conference proceedings, ten patents, and 12 books. His research interests include the Internet of Things, blockchain, wireless sensor networks, medical image processing, and machine learning.

Raghu Ramamoorthy, PhD is an Associate Professor in the Department of Computer Science and Engineering at the Oxford College of Engineering, Bengaluru, India. With more than 15 years of experience across academia and industry, he has published more than 50 articles in international journals and conferences. His research interests include VANETS, MANETS, computer networks, and wireless communication.

Rajesh Kumar Dhanaraj, PhD is a Professor at the Symbiosis Institute of Computer Studies and Research at Symbiosis International University, Pune, India. He has authored and edited more than 50 books and more than 115 articles on various cutting-edge technologies and holds 22 patents. His research interests encompass machine learning, cyber-physical systems, and wireless sensor networks.

Manjula Arunraj, PhD is an Assistant Professor in the Department of Health Information Management and Technology in the College of Applied Medical Sciences at King Faizal University, Alahsa, Saudi Arabia. Her research interests include clinical psychology, health psychology, and abnormal psychology.

Table of Contents
Preface
1. Cyber-Physical Human Systems: Overview, Fundamental Concepts and Perspectives
Anitha Velu, Raghu Ramamoorthy, R. China Appala Naidu and Rashad G. Abaszade
1.1 A Run-Through about Cyber-Physical Human Systems
1.1.1 Physical Process
1.1.2 Sensors and Actuators
1.1.3 Data Transmission
1.1.4 Computational Nodes
1.1.5 Control Algorithms
1.2 Fundamental Concepts of CPHS
1.2.1 Cyber-Physical Integration
1.2.2 Human-in-the-Loop
1.2.3 Sensing and Perception
1.2.4 Computation and Control
1.2.5 Communication and Networking
1.2.6 Safety, Security, and Privacy
1.2.7 Adaptivity and Learning
1.2.8 User Interface and Experience (UI/UX)
1.2.9 System Feedback Loops
1.2.10 Trust and Ethics
1.3 Key Features of CPHS
1.3.1 Reactive Computation
1.3.2 Network Connectivity
1.3.3 Robustness and Reliability
1.3.4 Concurrency
1.3.5 Real-Time Calculation
1.3.6 Safety-Critical Application
1.4 Application Areas of CPHS
1.4.1 Transport Systems
1.4.2 Healthcare
1.4.3 Smart Cities, Homes, and Farming
1.4.4 Emergency System
1.4.5 Education
1.5 Overview of the Book
1.6 Summary
References
2. Features, Characteristics, Architecture, and Future Potential of Cyber-Physical Human Systems
B. Srilatha, Raghu Ramamoorthy, Mary Francy Joseph and Anitha Velu
2.1 Introduction
2.2 Properties of Cyber-Physical Human Systems
2.3 Features of Cyber-Physical Human Systems
2.3.1 Real-Time Data Collection and Adaptive Feedback
2.3.2 Autonomous Decision Support
2.3.3 Human-Centric System Design
2.3.4 Collaborative Multi-Agent Ecosystem
2.3.5 Ethical and Context-Aware Behavior
2.4 Architecture of Cyber-Physical Human Systems
2.5 Cyber Layer (Computational Intelligence)
2.6 The Role of Computational Intelligence
2.6.1 Core Components of the Cyber Layer in CPHS
2.7 Architectural Frameworks
2.7.1 Cloud-Based Architecture
2.7.2 Hybrid Cloud-Edge Model
2.7.3 Key Challenges
2.8 Physical Layer
2.8.1 Key Components of the Physical Layer in CPHS
2.8.2 Functions of the Physical Layer
2.9 Integration with the Cyber Layer
2.10 The Human Layer: Cognitive and Decision-Making Role
2.10.1 Core Functions of the Human Layer in CPHS
2.10.2 Cognitive and Decision-Making Elements
2.10.3 Modes of Human Interaction
2.10.4 Design Considerations for Human Integration
2.10.5 Collaborative Decision-Making
2.10.6 Challenges in the Human Layer
2.11 The Future Potential of Cyber-Physical Human Systems
2.11.1 Personalized Healthcare and Assisted Living
2.11.2 Industry 5.0: Human-Centric Manufacturing
2.11.3 Autonomous and Connected Mobility
2.11.4 Smart Environments and Urban Resilience
2.11.5 Human-Machine Collaborative Decision-Making
2.11.6 Challenges and Research Directions
2.12 Conclusion
References
3. Technological Aspects of Advanced Human–Machine Interface for Cyberphysical System Implementation
B. Srilatha, Anitha Velu, Thanka Saranya C. and Meghana Karri
3.1 Introduction
3.2 HMI in Cyberphysical Human Systems
3.2.1 Contemporary HMI Architecture in CPS
3.3 Technological Aspects of Advanced HMI for CPS
3.3.1 Multimodal Interaction in Advanced HMI for CPS
3.3.2 Sensor Integration in Advanced HMI for CPHS
3.3.3 AR and VR in Advanced HMI for CPHS
3.4 Enabling Technologies for Advanced HMI in CPS
3.4.1 AI and ML in Advanced HMI for CPS
3.4.2 Digital Twin Technology in Advanced HMI for CPS
3.4.3 Edge Computing and Cloud Integration in Advanced HMI for CPS
3.5 Real-World Applications/Some Use Cases
3.5.1 Industrial Manufacturing
3.5.2 Occupational Safety and Health
3.5.3 Smart Home Systems
3.6 Challenges
3.7 Conclusion
References
4. Supervisory Control and Data Acquisition Network for Intrusion Detection in Cyberphysical Human Systems
N. Sathish, P. Arul, V. Yokesh and Rajesh Kumar Dhanaraj
4.1 Introduction
4.1.1 Overview of Cyberphysical Human Systems
4.1.2 Importance of SCADA in CPHS
4.1.3 Rising Complexity and Connectivity of SCADA Systems
4.1.4 Increasing Attack Surface and Real-World Incidents
4.2 SCADA Networks in CPHS: Architecture and Functionality
4.2.1 Components of a Typical SCADA System
4.2.2 Integration with IoT, CPS, Cloud, and Human Interfaces
4.2.3 Communication Protocols in SCADA Systems
4.2.4 Data Flow in SCADA and Its Role in Monitoring/Control
4.2.5 Role of Humans in the Loop: Decision-Making and Interaction
4.3 Cybersecurity Threat Landscape for SCADA in CPHS
4.3.1 Taxonomy of Threats: External, Internal, Intentional, and Accidental
4.3.2 Common Attack Vectors
4.3.3 Case Studies of Real-World SCADA Attacks
4.3.4 Challenges in Securing SCADA Systems in CPHS
4.4 Intrusion Detection Systems for SCADA in CPHS
4.4.1 Limitations of Traditional Security Measures
4.4.2 Types of Intrusion Detection Systems
4.4.3 Role of Machine Learning and AI in Intrusion Detection
4.4.4 Dataset Challenges and Training Considerations
4.5 Advanced Defense Mechanisms in SCADA-Enabled CPHS
4.5.1 Behavioral Modeling and Anomaly Detection
4.5.2 Blockchain for Decentralized Trust and Data Integrity
4.5.3 Adaptive and Self-Healing Security Architectures
4.5.4 Human-Centric and Explainable Defense Systems
4.6 Integration with Cloud and Edge Environments
4.6.1 Cloud Integration: Benefits and Security Implications
4.6.2 Edge Computing for Real-Time Intrusion Detection
4.6.3 Hybrid Architectures for SCADA Security
4.6.4 Challenges in Multi-Cloud and Multi-Edge Security Coordination
4.7 Human Factors and Usability in Intrusion Detection
4.7.1 The Role of Human Operators in SCADA Security
4.7.2 Human-in-the-Loop and Human-on-the-Loop Models
4.7.3 Designing Usable Intrusion Detection Systems
4.7.4 Training, Awareness, and Organizational Readiness
4.7.5 Ethical and Privacy Considerations in Human Monitoring
4.8 Case Studies and Real-World Applications
4.8.1 Industrial Control Systems—Stuxnet and Beyond
4.9 Future Directions and Research Challenges in SCADA Security for CPHS
4.10 Conclusion and Summary
References
5. CPHS–IoT Models: A Hybrid Approach to Deal with Engineering Systems and Control Modules
L.K. Indumathi, Merugu Praveen Kumar, Sonal Bhide and P. Manjula
5.1 Introduction
5.1.1 Cyberphysical Systems and Hybrid Systems
5.2 Basics of CPHS and IoT
5.2.1 Cyberphysical-Human Systems
5.2.2 Internet of Things
5.2.3 Inspiration for Hybrid CPHS–IoT Models
5.3 Hybrid CPHS–IoT Modeling Framework
5.3.1 Application Layer
5.3.2 Important Patterns of Execution
5.3.3 Cyber Layer in Hybrid CPHS–IoT Architecture
5.4 Physical Layer in Hybrid CPHS–IoT Architecture
5.4.1 Step-by-Step Workflow of the Physical Layer
5.4.2 Important Characteristics of the Physical Layer
5.5 Communication Layer in Hybrid CPHS–IoT Architecture
5.5.1 End-to-End Communication Flow Example for Smart Manufacturing
5.6 Sensor Layer in CPHS–IoT Architecture
5.6.1 Operating Mechanism
5.6.2 Examples of Uses
5.7 Implementation Strategies
5.7.1 Design Principles
5.7.2 Protocols and Standards
5.8 Case Study of Smart Agriculture Using CPHS–IoT System
5.8.1 Objective
5.8.2 Layer-Wise Implementation
5.8.3 Smart Agriculture CPHS–IoT System
5.9 Advantages and Challenges
5.9.1 Advantages
5.9.2 CPHS–IoT Challenges
5.10 Conclusion
References
6. Training Deep Neural Networks for Spatiotemporal Cyberphysical Human Systems
N. Sathish, R. Nagaraj, P. Arul and Pham Chien Thang
6.1 Foundations of Spatiotemporal Cyberphysical Human Systems
6.1.1 Evolution and Definition
6.1.2 Human-in-the-Loop System Dynamics
6.1.3 Role of AI and Deep Learning
6.2 Nature and Characteristics of Spatiotemporal Data
6.2.1 Temporal Dynamics: Trends, Sequences, and Cycles
6.2.2 Spatial Context: Structure, Location, and Interactions
6.2.3 Multimodal Data Fusion (Audio, Visual, Sensor, Text)
6.3 Deep Neural Network Architectures for CPHS
6.3.1 Temporal Modeling with RNNs, LSTMs, and GRUs
6.3.2 Spatiotemporal Fusion Using 3D-CNNs and ConvLSTMs
6.3.3 Attention Mechanisms and Transformer-Based Models
6.3.4 Hybrid Models for Complex Human-Centric Tasks
6.4 Architectures for Spatiotemporal Modeling
6.4.1 Recurrent Neural Networks (RNN, LSTM, GRU)
6.4.2 Convolutional Neural Networks for Spatial Patterns
6.4.3 Graph Neural Networks for Structured CPHS
6.4.4 Transformer-Based Models for Spatiotemporal Learning
6.5 Training Deep Networks for CPHS
6.5.1 Dataset Design and Annotation Strategies
6.5.2 Supervised, Unsupervised, and Self-Supervised Training
6.5.3 Transfer Learning and Domain Adaptation
6.5.4 Optimization Techniques and Model Tuning
6.5.5 Loss Functions for Spatiotemporal Tasks
6.5.6 Regularization and Overfitting Control
6.5.7 Hyperparameter Optimization Methods
6.6 Edge and Real-Time Deployment Considerations
6.6.1 Model Compression and Pruning
6.6.2 Training on Edge Devices
6.6.3 Latency-Aware Inference for Human-in-the-Loop Systems
6.7 Future Trends and Research Opportunities
6.7.1 Ethical and Privacy Concerns
6.7.2 Robustness, Explainability, and Trustworthiness
6.7.3 Scalability for Real-World Deployments
6.8 Conclusion
References
7. Digital Twin–Driven Model Architecture for Cyberphysical Systems Toward the Development of Testbed
Asha Kumari Ablikatte, Prajwal Balakrishna, Saravana Kumar E. and P. Bindu Madhavi
7.1 Introduction
7.1.1 Industry 5.0
7.1.2 Digital Twin
7.1.3 Applications of DT
7.2 CPHS in the Present Era
7.3 Digital Twin Test Beds
7.3.1 Architecture of Digital Twin Testbed
7.3.2 Communication Protocols in Testbeds
7.4 Experimental Scenarios
7.4.1 Controller Tuning
7.4.2 Fault Injection
7.4.3 Cybersecurity Testing
7.4.4 Model Calibration
7.4.5 Resilience Assessment
7.5 Conclusion
7.6 Future Scope
References
8. A Holistic Review of Ethical, Legal, and Social Implications of Emerging Cyberphysical Human Systems
Bhuvana B. P., Bharathi Raju, V. Yokesh and Ahmed A. Elngar
8.1 Introduction
8.2 Ethical Consideration for CPS
8.2.1 Autonomy and Accountability
8.2.2 Privacy and Surveillance
8.2.3 Safety and Security
8.2.4 Bias, Fairness, and Inclusivity
8.2.5 Human Dignity and Control
8.2.6 Environmental and Social Sustainability
8.2.7 Ethical Frameworks
8.2.8 Transparency and Explainability
8.2.9 Ethical Design in Emergencies and Critical Scenarios
8.2.10 Digital Divide and Access Inequality
8.2.11 Informed Consent and User Awareness
8.3 Legal Implications of CPS
8.3.1 Case Studies on the Legal Dimensions of Cyberphysical Systems
8.3.2 Evolving Legal Standards and Communication Protocols in CPSs
8.3.3 Current Gaps in Laws Governing CPS
8.3.4 Evolving Legal Frameworks for Secure Cyberphysical Systems
8.4 Social Implications of Cyberphysical Systems
8.4.1 Society 4.0
8.4.2 Society 5.0
8.4.3 CPS: Bridging Society 4.0 and Society 5.0
8.5 Conclusion
References
9. Addressing Challenges and Issues in Data Privacy and Cyberphysical Security Systems in Real-World Implementation
Lekshmi A. C., Koppala Guravaiah and Shashidhara R.
9.1 Introduction
9.2 Architectural Framework of CPHS
9.2.1 Perception Layer
9.2.2 Transmission Layer
9.2.3 Application Layer
9.3 Data Privacy in CPHS
9.3.1 Data Privacy Challenges
9.3.2 Privacy-Preserving Methods in CPHSs
9.3.3 Privacy Challenges in Real-World Implementations
9.4 Cyberphysical Security Systems
9.4.1 CPHS: Threats and Vulnerabilities
9.4.2 Security Attacks in CPHS
9.4.3 Security Challenges in Real-World Implementations
9.5 CPHS Risk Evaluation
9.6 Future Directions and Recommendations
9.7 Conclusion
References
10. Mist and Edge Computing Cyberphysical Human-Centered Systems for Industry 5.0 Application
Prajwal B., Raghu Ramamoorthy, Anitha Velu and P. Bindhu Madhavi
10.1 Introduction
10.1.1 Background of Industry 5.0
10.2 Edge Computing
10.3 Mist Computing
10.4 Mist, Edge, and Cloud Computing in Industry 5.0
10.5 Cyberphysical Human-Centered Systems
10.5.1 Applications of CPHCS
10.5.2 Security, Privacy, and Ethical Implications
10.5.3 XAI in CPHCS
10.6 Conclusion
References
11. An Intelligent Cyberphysical System Threat Detection for Secured Communication in 6G Autonomous Vehicle Networks
N. Sathish, A. Prasanth, V. Yokesh and Afizan Bin Azman
11.1 Cyberphysical Systems in 6G Autonomous Vehicle Networks
11.1.1 Introduction to Cyberphysical Systems
11.1.2 Role of CPS in Autonomous Vehicles
11.1.3 Impact of 6G on CPS Integration and Performance
11.2 Security Challenges in 6G-Enabled CPS for Autonomous Vehicles
11.2.1 Threat Landscape: APTs, Spoofing, DoS, and Data Breaches
11.2.2 Limitations of Traditional Security Mechanisms
11.3 Real-Time and Distributed Threat Dynamics
11.3.1 Threat Detection and Response in Real Time
11.3.2 Additional Dynamics of Distributed Attacks in the 6G Ecosystem
11.4 Intelligent Threat Detection Framework
11.4.1 Architecture of the Threat Detection System
11.4.2 Key Components: Sensors, Actuators, and Communication Interfaces
11.4.3 Threat Detection Life Cycle
11.5 Artificial Intelligence and Machine Learning for Threat Detection
11.5.1 Supervised and Unsupervised Learning Models
11.5.2 Deep Learning for Anomaly Detection
11.5.3 Real-Time Intrusion Prevention Mechanisms
11.6 Emerging Technologies for Secure Communication
11.6.1 Federated Learning for Privacy-Preserving Security
11.6.2 Blockchain for Trust and Data Integrity
11.6.3 Quantum-Safe Cryptography for Future-Proofing
11.7 Integration of Intelligent Systems in 6G Autonomous Networks
11.7.1 System Interoperability and Latency Considerations
11.7.2 Edge Computing and Distributed Processing
11.7.3 Network Slicing and Security Customization
11.8 Challenges and Future Research Directions
11.8.1 Scalability and Adaptability of Detection Models
11.8.2 Evolving Threats in AI-Driven AV Ecosystems
11.8.3 Roadmap to Fully Autonomous Secure Transportation
11.9 Conclusion
References
12. Case Study on Cyberphysical Human Systems for Safe Human–Robot Collaboration in a Shared Workplace
H. Nishanthi, P. Divya Prabha, N. Nandhine Shree and Raffaele Mascella
12.1 A Run through of Human–Robot Interaction
12.2 Challenges of HMI
12.3 Importance of Safe Human–Robot Collaboration
12.4 Types of Robots Used in Collaborative Setup
12.4.1 Collaborative Industrial Robots (Cobots)
12.4.2 Mobile Robots (Autonomous Mobile Robots and Automated Guided Vehicles)
12.4.3 Dual-Arm Robots
12.4.4 Wearable Robotic Systems (Exoskeletons)
12.4.5 Assistive Robotic Arms
12.5 System Architecture of Cyberphysical Human System
12.5.1 Physical Layer
12.5.2 Sensing and Perception Layer
12.5.3 Data Processing and Processing Layer
12.5.4 Control and Decision-Making Layer
12.5.5 Communication Layer
12.5.6 User Interface and Monitoring Layer
12.6 Sensors and Perception Systems for Human Detection
12.6.1 Depth Cameras and Vision-Based Systems
12.6.2 LiDAR
12.6.3 Ultrasonic and Infrared Sensors
12.6.4 Use of Wearable Sensors and Tags
12.6.5 Sensor Fusion for Better Perception
12.6.6 Real-Time Human Tracking Algorithms
12.7 Human–Robot Interaction Models and Strategies
12.7.1 Interaction Models
12.7.2 Interaction Strategies
12.8 Implementation
12.9 Risk Assessment and Hazard Mitigation Techniques
12.9.1 Understanding Risk Assessment in Human–Robot Collaboration
12.9.2 Significant Steps in Risk Assessment
12.9.3 Hazard Mitigation Strategies
12.9.4 Continuous Safety Validation and Improvement
12.10 Machine Learning and AI Integration for Dynamic Decision-Making
12.10.1 The Function of Artificial Intelligence in Collaborative Robotics
12.10.2 Machine Learning for Ease of Development and Learning in Work Cells
12.10.3 Real-Time Data Processing and Decision Support
12.11 Challenges and Lessons Learned from Implementation
12.11.1 Sensor Limitations and Reliability of Data
12.11.2 Real-Time Constraints
12.11.3 Integration with Legacy Systems
12.11.4 Human Factors and Operator Trust
12.11.5 Complexity of Risk Assessment
12.11.6 System Scalability
12.12 Test Cases of CPHS with HMI
12.13 Conclusion
References
13. Medical Cyberphysical Systems: A Solution to Smart Health Using Flexible Sensors and Devices
Prema S. and G. Krishnapriya
13.1 Introduction
13.2 Background on MCPSs and Smart Health for the Elderly
13.2.1 Major Elements of an MCPSs for Intelligent Health
13.3 System Architecture of Smart Medical Cyberphysical Systems Framework
13.3.1 System Architecture Workflow
13.4 Smart Health Solutions Using MCPSs and Emerging Trends
13.4.1 XAI in MCPSs
13.4.2 Digital Twins
13.4.3 CPS-Driven Transformation Toward Society 5.0
13.4.4 Intrusion Detection System
13.5 Benefits and Applications of MCPSs in Health Optimization
13.6 Conclusions and Future Directions
References
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Cyber Physical Human Systems