Cyber-Physical-Human Systems: Fundamentals and Applications

Cover
Title Page
Copyright
Contents
A Note from the Series Editor
About the Editors
List of Contributors
Introduction
Part I Fundamental Concepts and Methods
Chapter 1 Human‐in‐the‐Loop Control and Cyber–Physical–Human Systems: Applications and Categorization
1.1 Introduction
1.2 Cyber + Physical + Human
1.2.1 Cyberphysical Systems
1.2.2 Physical–Human Systems
1.2.3 Cyber–Human Systems
1.3 Categorizing Human‐in‐the‐Loop Control Systems
1.3.1 Human‐in‐the‐Plant
1.3.2 Human‐in‐the‐Controller
1.3.3 Human–Machine Control Symbiosis
1.3.4 Humans‐in‐Multiagent‐Loops
1.4 A Roadmap for Human‐in‐the‐Loop Control
1.4.1 Self‐ and Human‐Driven Cars on Urban Roads
1.4.2 Climate Change Mitigation and Smart Grids
1.5 Discussion
1.5.1 Other Ways of Classifying Human‐in‐the‐Loop Control
1.5.2 Modeling Human Understanding and Decision‐Making
1.5.3 Ethics and CPHS
1.6 Conclusions
Acknowledgments
References
Chapter 2 Human Behavioral Models Using Utility Theory and Prospect Theory
2.1 Introduction
2.2 Utility Theory
2.2.1 An Example
2.3 Prospect Theory
2.3.1 An Example: CPT Modeling for SRS
2.3.1.1 Detection of CPT Effects via Lotteries
2.3.2 Theoretical Implications of CPT
2.3.2.1 Implication I: Fourfold Pattern of Risk Attitudes
2.3.2.2 Implication II: Strong Risk Aversion Over Mixed Prospects
2.3.2.3 Implication III: Effects of Self‐Reference
2.4 Summary and Conclusions
Acknowledgments
References
Chapter 3 Social Diffusion Dynamics in Cyber–Physical–Human Systems
3.1 Introduction
3.2 General Formalism for Social Diffusion in CPHS
3.2.1 Complex and Multiplex Networks
3.2.2 General Framework for Social Diffusion
3.2.3 Main Theoretical Approaches
3.3 Modeling Decision‐Making
3.3.1 Pairwise Interaction Models
3.3.2 Linear Threshold Models
3.3.3 Game‐Theoretic Models
3.4 Dynamics in CPHS
3.4.1 Social Diffusion in Multiplex Networks
3.4.2 Co‐Evolutionary Social Dynamics
3.5 Ongoing Efforts Toward Controlling Social Diffusion and Future Challenges
Acknowledgments
References
Chapter 4 Opportunities and Threats of Interactions Between Humans and Cyber–Physical Systems – Integration and Inclusion Approaches for CPHS
4.1 CPHS and Shared Control
4.2 “Tailor‐made” Principles for Human–CPS Integration
4.3 “All‐in‐one” based Principles for Human–CPS Inclusion
4.4 Dissonances, Opportunities, and Threats in a CPHS
4.5 Examples of Opportunities and Threats
4.6 Conclusions
References
Chapter 5 Enabling Human‐Aware Autonomy Through Cognitive Modeling and Feedback Control
5.1 Introduction
5.1.1 Important Cognitive Factors in HAI
5.1.2 Challenges with Existing CPHS Methods
5.1.3 How to Read This Chapter
5.2 Cognitive Modeling
5.2.1 Modeling Considerations
5.2.2 Cognitive Architectures
5.2.3 Computational Cognitive Models
5.2.3.1 ARMAV and Deterministic Linear Models
5.2.3.2 Dynamic Bayesian Models
5.2.3.3 Decision Analytical Models
5.2.3.4 POMDP Models
5.3 Study Design and Data Collection
5.3.1 Frame Research Questions and Identify Variables
5.3.2 Formulate Hypotheses or Determine the Data Needed
5.3.2.1 Hypothesis Testing Approach
5.3.2.2 Model Training Approach
5.3.3 Design Experiment and/or Study Scenario
5.3.3.1 Hypothesis Testing Approach
5.3.3.2 Model Training Approach
5.3.4 Conduct Pilot Studies and Get Initial Feedback; Do Preliminary Analysis
5.3.5 A Note about Institutional Review Boards and Recruiting Participants
5.4 Cognitive Feedback Control
5.4.1 Considerations for Feedback Control
5.4.2 Approaches
5.4.2.1 Heuristics‐Based Planning
5.4.2.2 Measurement‐Based Feedback
5.4.2.3 Goal‐Oriented Feedback
5.4.2.4 Case Study
5.4.3 Evaluation Methods
5.5 Summary and Opportunities for Further Investigation
5.5.1 Model Generalizability and Adaptability
5.5.2 Measurement of Cognitive States
5.5.3 Human Subject Study Design
References
Chapter 6 Shared Control with Human Trust and Workload Models
6.1 Introduction
6.1.1 Review of Shared Control Methods
6.1.2 Contribution and Approach
6.1.3 Review of IRL Methods Under Partial Information
6.1.3.1 Organization
6.2 Preliminaries
6.2.1 Markov Decision Processes
6.2.2 Partially Observable Markov Decision Processes
6.2.3 Specifications
6.3 Conceptual Description of Shared Control
6.4 Synthesis of the Autonomy Protocol
6.4.1 Strategy Blending
6.4.2 Solution to the Shared Control Synthesis Problem
6.4.2.1 Nonlinear Programming Formulation for POMDPs
6.4.2.2 Strategy Repair Using Sequential Convex Programming
6.4.3 Sequential Convex Programming Formulation
6.4.4 Linearizing Nonconvex Problem
6.4.4.1 Linearizing Nonconvex Constraints and Adding Slack Variables
6.4.4.2 Trust Region Constraints
6.4.4.3 Complete Algorithm
6.4.4.4 Additional Specifications
6.4.4.5 Additional Measures
6.5 Numerical Examples
6.5.1 Modeling Robot Dynamics as POMDPs
6.5.2 Generating Human Demonstrations
6.5.3 Learning a Human Strategy
6.5.4 Task Specification
6.5.5 Results
6.6 Conclusion
Acknowledgments
References
Chapter 7 Parallel Intelligence for CPHS: An ACP Approach
7.1 Background and Motivation
7.2 Early Development in China
7.3 Key Elements and Framework
7.4 Operation and Process
7.4.1 Construction of Artificial Systems
7.4.2 Computational Experiments in Parallel Intelligent Systems
7.4.3 Closed‐Loop Optimization Based on Parallel Execution
7.5 Applications
7.5.1 Parallel Control and Intelligent Control
7.5.2 Parallel Robotics and Parallel Manufacturing
7.5.3 Parallel Management and Intelligent Organizations
7.5.4 Parallel Medicine and Smart Healthcare
7.5.5 Parallel Ecology and Parallel Societies
7.5.6 Parallel Economic Systems and Social Computing
7.5.7 Parallel Military Systems
7.5.8 Parallel Cognition and Parallel Philosophy
7.6 Conclusion and Prospect
References
Part II Transportation
Chapter 8 Regularities of Human Operator Behavior and Its Modeling
8.1 Introduction
8.2 The Key Variables in Man–Machine Systems
8.3 Human Responses
8.4 Regularities of Man–Machine System in Manual Control
8.4.1 Man–Machine System in Single‐loop Compensatory System
8.4.2 Man–Machine System in Multiloop, Multichannel, and Multimodal Tasks
8.4.2.1 Man–Machine System in the Multiloop Tracking Task
8.4.2.2 Man–Machine System in the Multichannel Tracking Task
8.4.2.3 Man–Machine System in Multimodal Tracking Tasks
8.4.2.4 Human Operator Behavior in Pursuit and Preview Tracking Tasks
8.5 Mathematical Modeling of Human Operator Behavior in Manual Control Task
8.5.1 McRuer's Model for the Pilot Describing Function
8.5.1.1 Single‐Loop Compensatory Model
8.5.1.2 Multiloop and Multimodal Compensatory Model
8.5.2 Structural Human Operator Model
8.5.3 Pilot Optimal Control Model
8.5.4 Pilot Models in Preview and Pursuit Tracking Tasks
8.6 Applications of the Man–Machine System Approach
8.6.1 Development of Criteria for Flying Qualities and PIO Prediction
8.6.1.1 Criteria of FQ and PIO Prediction as a Requirement for the Parameters of the Pilot‐Aircraft System
8.6.1.2 Calculated Piloting Rating of FQ as the Criteria
8.6.2 Interfaces Design
8.6.3 Optimization of Control System and Vehicle Dynamics Parameters
8.7 Future Research Challenges and Visions
8.8 Conclusion
References
Chapter 9 Safe Shared Control Between Pilots and Autopilots in the Face of Anomalies
9.1 Introduction
9.2 Shared Control Architectures: A Taxonomy
9.3 Recent Research Results
9.3.1 Autopilot
9.3.1.1 Dynamic Model of the Aircraft
9.3.1.2 Advanced Autopilot Based on Adaptive Control
9.3.1.3 Autopilot Based on Proportional Derivative Control
9.3.2 Human Pilot
9.3.2.1 Pilot Models in the Absence of Anomaly
9.3.2.2 Pilot Models in the Presence of Anomaly
9.3.3 Shared Control
9.3.3.1 SCA1: A Pilot with a CfM‐Based Perception and a Fixed‐Gain Autopilot
9.3.3.2 SCA2: A Pilot with a CfM‐Based Decision‐Making and an Advanced Adaptive Autopilot
9.3.4 Validation with Human‐in‐the‐Loop Simulations
9.3.5 Validation of Shared Control Architecture 1
9.3.5.1 Experimental Setup
9.3.5.2 Anomaly
9.3.5.3 Experimental Procedure
9.3.5.4 Details of the Human Subjects
9.3.5.5 Pilot‐Model Parameters
9.3.5.6 Results and Observations
9.3.6 Validation of Shared Control Architecture 2
9.3.6.1 Experimental Setup
9.3.6.2 Anomaly
9.3.6.3 Experimental Procedure
9.3.6.4 Details of the Human Subjects
9.3.6.5 Results and Observations
9.4 Summary and Future Work
References
Chapter 10 Safe Teleoperation of Connected and Automated Vehicles
10.1 Introduction
10.2 Safe Teleoperation
10.2.1 The Advent of 5G
10.3 CPHS Design Challenges in Safe Teleoperation
10.4 Recent Research Advances
10.4.1 Enhancing Operator Perception
10.4.2 Safe Shared Autonomy
10.5 Future Research Challenges
10.5.1 Full Utilization of V2X Networks
10.5.2 Mixed Autonomy Traffic Modeling
10.5.3 5G Experimentation
10.6 Conclusions
References
Chapter 11 Charging Behavior of Electric Vehicles
11.1 History, Challenges, and Opportunities
11.1.1 The History and Status Quo of EVs
11.1.2 The Current Challenge
11.1.3 The Opportunities
11.2 Data Sets and Problem Modeling
11.2.1 Data Sets of EV Charging Behavior
11.2.1.1 Trend Data Sets
11.2.1.2 Driving Data Sets
11.2.1.3 Battery Data Sets
11.2.1.4 Charging Data Sets
11.2.2 Problem Modeling
11.3 Control and Optimization Methods
11.3.1 The Difficulty of the Control and Optimization
11.3.2 Charging Location Selection and Routing Optimization
11.3.3 Charging Process Control
11.3.4 Control and Optimization Framework
11.3.4.1 Centralized Optimization
11.3.4.2 Decentralized Optimization
11.3.4.3 Hierarchical Optimization
11.3.5 The Impact of Human Behaviors
11.4 Conclusion and Discussion
References
Part III Robotics
Chapter 12 Trust‐Triggered Robot–Human Handovers Using Kinematic Redundancy for Collaborative Assembly in Flexible Manufacturing
12.1 Introduction
12.2 The Task Context and the Handover
12.3 The Underlying Trust Model
12.4 Trust‐Based Handover Motion Planning Algorithm
12.4.1 The Overall Motion Planning Strategy
12.4.2 Manipulator Kinematics and Kinetics Models
12.4.3 Dynamic Impact Ellipsoid
12.4.4 The Novel Motion Control Approach
12.4.5 Illustration of the Novel Algorithm
12.5 Development of the Experimental Settings
12.5.1 Experimental Setup
12.5.1.1 Type I: Center Console Assembly
12.5.1.2 Type II: Hose Assembly
12.5.2 Real‐Time Measurement and Display of Trust
12.5.2.1 Type I: Center Console Assembly
12.5.2.2 Type II: Hose Assembly
12.5.2.3 Trust Computation
12.5.3 Plans to Execute the Trust‐Triggered Handover Strategy
12.5.3.1 Type I Assembly
12.5.3.2 Type II Assembly
12.6 Evaluation of the Motion Planning Algorithm
12.6.1 Objective
12.6.2 Experiment Design
12.6.3 Evaluation Scheme
12.6.4 Subjects
12.6.5 Experimental Procedures
12.6.5.1 Type I Assembly
12.6.5.2 Type II Assembly
12.7 Results and Analyses, Type I Assembly
12.8 Results and Analyses, Type II Assembly
12.9 Conclusions and Future Work
Acknowledgment
References
Chapter 13 Fusing Electrical Stimulation and Wearable Robots with Humans to Restore and Enhance Mobility
13.1 Introduction
13.1.1 Functional Electrical Stimulation
13.1.2 Spinal Cord Stimulation
13.1.3 Wearable Robotics (WR)
13.1.4 Fusing FES/SCS and Wearable Robotics
13.2 Control Challenges
13.2.1 Feedback Approaches to Promote Volition
13.2.2 Principles of Assist‐as‐Needed
13.2.3 Tracking Control Problem Formulation
13.2.4 Co‐operative Control Strategies
13.2.5 EMG‐ and MMG‐Based Assessment of Muscle Activation
13.3 Examples
13.3.1 A Hybrid Robotic System for Arm Training of Stroke Survivors
13.3.2 First Certified Hybrid Robotic Exoskeleton for Gait Rehabilitation Settings
13.3.3 Body Weight‐Supported Robotic Gait Training with tSCS
13.3.4 Modular FES and Wearable Robots to Customize Hybrid Solutions
13.4 Transfer into Daily Practice: Integrating Ethical, Legal, and Societal Aspects into the Design
13.5 Summary and Outlook
Acknowledgments
Acronyms
References
Chapter 14 Contemporary Issues and Advances in Human–Robot Collaborations
14.1 Overview of Human–Robot Collaborations
14.1.1 Task Architecture
14.1.2 Human–Robot Team Formation
14.1.3 Human Modeling: Control and Decision
14.1.4 Human Modeling: Other Human Factors
14.1.5 Industrial Perspective
14.1.6 What Is in This Chapter
14.2 Passivity‐Based Human‐Enabled Multirobot Navigation
14.2.1 Architecture Design
14.2.2 Human Passivity Analysis
14.2.3 Human Workload Analysis
14.3 Operation Support with Variable Autonomy via Gaussian Process
14.3.1 Design of the Operation Support System with Variable Autonomy
14.3.2 User Study
14.3.2.1 Operational Verification
14.3.2.2 Usability Test
14.4 Summary
Acknowledgments
References
Part IV Healthcare
Chapter 15 Overview and Perspectives on the Assessment and Mitigation of Cognitive Fatigue in Operational Settings
15.1 Introduction
15.2 Cognitive Fatigue
15.2.1 Definition
15.2.2 Origin of Cognitive Fatigue
15.2.3 Effects on Adaptive Capacities
15.3 Cyber–Physical System and Cognitive Fatigue: More Automation Does Not Imply Less Cognitive Fatigue
15.4 Assessing Cognitive Fatigue
15.4.1 Subjective Measures
15.4.2 Behavioral Measures
15.4.3 Physiological Measurements
15.5 Limitations and Benefits of These Measures
15.6 Current and Future Solutions and Countermeasures
15.6.1 Physiological Computing: Toward Real‐Time Detection and Adaptation
15.7 System Design and Explainability
15.8 Future Challenges
15.8.1 Generalizing the Results Observed in the Laboratory to Ecological Situations
15.8.2 Determining the Specificity of Cognitive Fatigue
15.8.3 Recovering from Cognitive Fatigue
15.9 Conclusion
References
Chapter 16 Epidemics Spread Over Networks: Influence of Infrastructure and Opinions
16.1 Introduction
16.1.1 Infectious Diseases
16.1.2 Modeling Epidemic Spreading Processes
16.1.3 Susceptible–Infected–Susceptible (SIS) Compartmental Models
16.2 Epidemics on Networks
16.2.1 Motivation
16.2.2 Modeling Epidemics over Networks
16.2.3 Networked Susceptible–Infected–Susceptible Epidemic Models
16.3 Epidemics and Cyber–Physical–Human Systems
16.3.1 Epidemic and Opinion Spreading Processes
16.3.2 Epidemic and Infrastructure
16.4 Recent Research Advances
16.4.1 Notation
16.4.2 Epidemic and Opinion Spreading Processes
16.4.2.1 Opinions Over Networks with Both Cooperative and Antagonistic Interactions
16.4.2.2 Coupled Epidemic and Opinion Dynamics
16.4.2.3 Opinion‐Dependent Reproduction Number
16.4.2.4 Simulations
16.4.3 Epidemic Spreading with Shared Resources
16.4.3.1 The Multi‐Virus SIWS Model
16.4.3.2 Problem Statements
16.4.3.3 Analysis of the Eradicated State of a Virus
16.4.3.4 Persistence of a Virus
16.4.3.5 Simulations
16.5 Future Research Challenges and Visions
References
Chapter 17 Digital Twins and Automation of Care in the Intensive Care Unit
17.1 Introduction
17.1.1 Economic Context
17.1.2 Healthcare Context
17.1.3 Technology Context
17.1.4 Overall Problem and Need
17.2 Digital Twins and CPHS
17.2.1 Digital Twin/Virtual Patient Definition
17.2.2 Requirements in an ICU Context
17.2.3 Digital Twin Models in Key Areas of ICU Care and Relative to Requirements
17.2.4 Review of Digital Twins in Automation of ICU Care
17.2.5 Summary
17.3 Role of Social‐Behavioral Sciences
17.3.1 Introduction
17.3.2 Barriers to Innovation Adoption
17.3.3 Ergonomics and Codesign
17.3.4 Summary (Key Takeaways)
17.4 Future Research Challenges and Visions
17.4.1 Technology Vision of the Future of CPHS in ICU Care
17.4.2 Social‐Behavioral Sciences Vision of the Future of CPHS in ICU Care
17.4.3 Joint Vision of the Future and Challenges to Overcome
17.5 Conclusions
References
Part V Sociotechnical Systems
Chapter 18 Online Attention Dynamics in Social Media
18.1 Introduction to Attention Economy and Attention Dynamics
18.2 Online Attention Dynamics
18.2.1 Collective Attention Is Limited
18.2.2 Skewed Attention Distribution
18.2.3 The Role of Novelty
18.2.4 The Role of Popularity
18.2.5 Individual Activity Is Bursty
18.2.6 Recommendation Systems Are the Main Gateways for Information
18.2.7 Change Is the Only Constant
18.3 The New Challenge: Understanding Recommendation Systems Effect in Attention Dynamics
18.3.1 Model Description
18.3.2 Results and Discussion
18.4 Conclusion
Acknowledgments
References
Chapter 19 Cyber–Physical–Social Systems for Smart City
19.1 Introduction
19.2 Social Community and Smart Cities
19.2.1 Smart Infrastructure
19.2.2 Smart Energy
19.2.3 Smart Transportation
19.2.4 Smart Healthcare
19.3 CPSS Concepts, Tools, and Techniques
19.3.1 CPSS Concepts
19.3.2 CPSS Tools
19.3.3 CPSS Techniques
19.3.3.1 IoT in Smart Cities
19.3.3.2 Big Data in Smart Cities
19.4 Recent Research Advances
19.4.1 Recent Research Advances of CASIA
19.4.2 Recent Research in European Union
19.4.3 Future Research Challenges and Visions
19.5 Conclusions
Acknowledgments
References
Part VI Concluding Remarks
Chapter 20 Conclusion and Perspectives
20.1 Benefits to Humankind: Synthesis of the Chapters and their Open Directions
20.2 Selected Areas for Current and Future Development in CPHS
20.2.1 Driver Modeling for the Design of Advanced Driver Assistance Systems
20.2.2 Cognitive Cyber–Physical Systems and CPHS
20.2.3 Emotion–Cognition Interactions
20.3 Ethical and Social Concerns: Few Directions
20.3.1 Frameworks for Ethics
20.3.2 Technical Approaches
20.4 Afterword
References
Index
EULA