Description
Book SynopsisHandbook of Human-Machine Systems Insightful and cutting-edge discussions of recent developments in human-machine systems
In Handbook of Human-Machine Systems, a team of distinguished researchers delivers a comprehensive exploration of human-machine systems (HMS) research and development from a variety of illuminating perspectives. The book offers a big picture look at state-of-the-art research and technology in the area of HMS. Contributing authors cover Brain-Machine Interfaces and Systems, including assistive technologies like devices used to improve locomotion. They also discuss advances in the scientific and engineering foundations of Collaborative Intelligent Systems and Applications.
Companion technology, which combines trans-disciplinary research in fields like computer science, AI, and cognitive science, is explored alongside the applications of human cognition in intelligent and artificially intelligent system designs, human factors engineering,
Table of Contents
Editors Biography xxi
List of Contributors xxiii
Preface xxxiii
1 Introduction 1
Giancarlo Fortino, David Kaber, Andreas Nürnberger, and David Mendonça
1.1 Book Rationale 1
1.2 Chapters Overview 2
Acknowledgments 8
References 8
2 Brain–Computer Interfaces: Recent Advances, Challenges, and Future Directions 11
Tiago H. Falk, Christoph Guger, and Ivan Volosyak
2.1 Introduction 11
2.2 Background 12
2.2.1 Active/Reactive BCIs 13
2.2.2 Passive BCIs 14
2.2.3 Hybrid BCIs 15
2.3 Recent Advances and Applications 15
2.3.1 Active/Reactive BCIs 15
2.3.2 Passive BCIs 16
2.3.3 Hybrid BCIs 16
2.4 Future Research Challenges 16
2.4.1 Current Research Issues 17
2.4.2 Future Research Directions 17
2.5 Conclusions 18
References 18
3 Brain–Computer Interfaces for Affective Neurofeedback Applications 23
Lucas R. Trambaiolli and Tiago H. Falk
3.1 Introduction 23
3.2 Background 23
3.3 State-of-the-Art 24
3.3.1 Depressive Disorder 25
3.3.2 Posttraumatic Stress Disorder, PTSD 26
3.4 Future Research Challenges 27
3.4.1 Open Challenges 27
3.4.2 Future Directions 28
3.5 Conclusion 28
References 29
4 Pediatric Brain–Computer Interfaces: An Unmet Need 35
Eli Kinney-Lang, Erica D. Floreani, Niloufaralsadat Hashemi, Dion Kelly, Stefanie S. Bradley, Christine Horner, Brian Irvine, Zeanna Jadavji, Danette Rowley, Ilyas Sadybekov, Si Long Jenny Tou, Ephrem Zewdie, Tom Chau, and Adam Kirton
4.1 Introduction 35
4.1.1 Motivation 36
4.2 Background 36
4.2.1 Components of a BCI 36
4.2.1.1 Signal Acquisition 36
4.2.1.2 Signal Processing 36
4.2.1.3 Feedback 36
4.2.1.4 Paradigms 37
4.2.2 Brain Anatomy and Physiology 37
4.2.3 Developmental Neurophysiology 38
4.2.4 Clinical Translation of BCI 38
4.2.4.1 Assistive Technology (AT) 38
4.2.4.2 Clinical Assessment 39
4.3 Current Body of Knowledge 39
4.4 Considerations for Pediatric BCI 40
4.4.1 Developmental Impact on EEG-based BCI 40
4.4.2 Hardware for Pediatric BCI 41
4.4.3 Signal Processing for Pediatric BCI 41
4.4.3.1 Feature Extraction, Selection and Classification 42
4.4.3.2 Emerging Techniques 42
4.4.4 Designing Experiments for Pediatric BCI 43
4.4.5 Meaningful Applications for Pediatric BCI 43
4.4.6 Clinical Translation of Pediatric BCI 44
4.5 Conclusions 44
References 45
5 Brain–Computer Interface-based Predator–Prey Drone Interactions 49
Abdelkader Nasreddine Belkacem and Abderrahmane Lakas
5.1 Introduction 49
5.2 Related Work 50
5.3 Predator–Prey Drone Interaction 51
5.4 Conclusion and Future Challenges 57
References 58
6 Levels of Cooperation in Human–Machine Systems: A Human–BCI–Robot Example 61
Marie-Pierre Pacaux-Lemoine, Lydia Habib, and Tom Carlson
6.1 Introduction 61
6.2 Levels of Cooperation 61
6.3 Application to the Control of a Robot by Thought 63
6.3.1 Designing the System 64
6.3.2 Experiments and Results 66
6.4 Results from the Methodological Point of View 67
6.5 Conclusion and Perspectives 68
References 69
7 Human–Machine Social Systems: Test and Validation via Military Use Cases 71
Charlene K. Stokes, Monika Lohani, Arwen H. DeCostanza, and Elliot Loh
7.1 Introduction 71
7.2 Background Summary: From Tools to Teammates 72
7.2.1 Two Sides of the Equation 72
7.2.2 Moving Beyond the Cognitive Revolution 73
7.2.2.1 A Rediscovery of the Unconscious 74
7.3 Future Research Directions 75
7.3.1 Machine: Functional Designs 75
7.3.2 Human: Ground Truth 76
7.3.2.1 Physiological Computing 76
7.3.3 Context: Tying It All Together 77
7.3.3.1 Training and Team Models 77
7.4 Conclusion 79
References 79
8 The Role of Multimodal Data for Modeling Communication in Artificial Social Agents 83
Stephanie Gross and Brigitte Krenn
8.1 Introduction 83
8.2 Background 84
8.2.1 Context 84
8.2.2 Basic Definitions 84
8.3 Related Work 84
8.3.1 HHI Data 85
8.3.2 HRI Data 85
8.3.2.1 Joint Attention and Robot Turn-Taking Capabilities 85
8.3.3 Public Availability of the Data 87
8.4 Datasets and Resulting Implications 87
8.4.1 Human Communicative Signals 87
8.4.1.1 Experimental Setup 87
8.4.1.2 Data Analysis and Results 88
8.4.2 Humans Reacting to Robot Signals 89
8.4.2.1 Comparing Different Robotic Turn-Giving Signals 89
8.4.2.2 Comparing Different Transparency Mechanisms 90
8.5 Conclusions 91
8.6 Future Research Challenges 91
References 91
9 Modeling Interactions Happening in People-Driven Collaborative Processes 95
Maximiliano Canche, Sergio F. Ochoa, Daniel Perovich, and Rodrigo Santos
9.1 Introduction 95
9.2 Background 97
9.3 State-of-the-Art in Interaction Modeling Languages and Notations 98
9.3.1 Visual Languages and Notations 99
9.3.2 Comparison of Interaction Modeling Languages and Notations 100
9.4 Challenges and Future Research Directions 101
References 102
10 Transparent Communications for Human–Machine Teaming 105
JessieY.C.Chen
10.1 Introduction 105
10.2 Definitions and Frameworks 105
10.3 Implementation of Transparent Human–Machine Interfaces in Intelligent Systems 106
10.3.1 Human–Robot Interaction 106
10.3.2 Multiagent Systems and Human–Swarm Interaction 108
10.3.3 Automated/Autonomous Driving 109
10.3.4 Explainable AI-Based Systems 109
10.3.5 Guidelines and Assessment Methods 109
10.4 Future Research Directions 110
References 111
11 Conversational Human–Machine Interfaces 115
María Jesús Rodríguez-Sánchez, Kawtar Benghazi, David Griol, and Zoraida Callejas
11.1 Introduction 115
11.2 Background 115
11.2.1 History of the Development of the Field 116
11.2.2 Basic Definitions 117
11.3 State-of-the-Art 117
11.3.1 Discussion of the Most Important Scientific/Technical Contributions 117
11.3.2 Comparison Table 119
11.4 Future Research Challenges 121
11.4.1 Current Research Issues 121
11.4.2 Future Research Directions Dealing with the Current Issues 121
References 122
12 Interaction-Centered Design: An Enduring Strategy and Methodology for Sociotechnical Systems 125
Ming Hou, Scott Fang, Wenbi Wang, and Philip S. E. Farrell
12.1 Introduction 125
12.2 Evolution of HMS Design Strategy 126
12.2.1 A HMS Technology: Intelligent Adaptive System 126
12.2.2 Evolution of IAS Design Strategy 128
12.3 State-of-the-Art: Interaction-Centered Design 130
12.3.1 A Generic Agent-based ICD Framework 130
12.3.2 IMPACTS: An Human–Machine Teaming Trust Model 132
12.3.3 ICD Roadmap for IAS Design and Development 133
12.3.4 ICD Validation, Adoption, and Contributions 134
12.4 IAS Design Challenges and Future Work 135
12.4.1 Challenges of HMS Technology 136
12.4.2 Future Work in IAS Design and Validation 136
References 137
13 Human–Machine Computing: Paradigm, Challenges, and Practices 141
Zhiwen Yu, Qingyang Li, and Bin Guo
13.1 Introduction 141
13.2 Background 142
13.2.1 History of the Development 142
13.2.2 Basic Definitions 143
13.3 State of the Art 144
13.3.1 Technical Contributions 144
13.3.2 Comparison Table 148
13.4 Future Research Challenges 150
13.4.1 Current Research Issues 150
13.4.2 Future Research Directions 151
References 152
14 Companion Technology 155
Andreas Wendemuth
14.1 Introduction 155
14.2 Background 155
14.2.1 History 156
14.2.2 Basic Definitions 157
14.3 State-of-the-Art 158
14.3.1 Discussion of the Most Important Scientific/Technical Contributions 159
14.4 Future Research Challenges 159
14.4.1 Current Research Issues 159
14.4.2 Future Research Directions Dealing with the Current Issues 160
References 161
15 A Survey on Rollator-Type Mobility Assistance Robots 165
Milad Geravand, Christian Werner, Klaus Hauer, and Angelika Peer
15.1 Introduction 165
15.2 Mobility Assistance Platforms 165
15.2.1 Actuation 166
15.2.2 Kinematics 166
15.2.2.1 Locomotion Support 166
15.2.2.2 STS Support 166
15.2.3 Sensors 168
15.2.4 Human–Machine Interfaces 168
15.3 Functionalities 168
15.3.1 STS Assistance 169
15.3.2 Walking Assistance 169
15.3.2.1 Maneuverability Improvement 169
15.3.2.2 Gravity Compensation 170
15.3.2.3 Obstacle Avoidance 170
15.3.2.4 Falls Risk Prediction and Fall Prevention 170
15.3.3 Localization and Navigation 170
15.3.3.1 Map Building and Localization 171
15.3.3.2 Path Planning 171
15.3.3.3 Assisted Localization 171
15.3.3.4 Assisted Navigation 171
15.3.4 Further Functionalities 171
15.3.4.1 Reminder Systems 171
15.3.4.2 Health Monitoring 171
15.3.4.3 Communication, Information, Entertainment, and Training 172
15.4 Conclusion 172
References 173
16 A Wearable Affective Robot 181
Jia Liu, Jinfeng Xu, Min Chen, and Iztok Humar
16.1 Introduction 181
16.2 Architecture Design and Characteristics 183
16.2.1 Architecture of a Wearable Affective Robot 183
16.2.2 Characteristics of a Wearable Affective Robot 184
16.3 Design of the Wearable, Affective Robot’s Hardware 185
16.3.1 AIWAC Box Hardware Design 185
16.3.2 Hardware Design of the EEG Acquisition 185
16.3.3 AIWAC Smart Tactile Device 185
16.3.4 Prototype of the Wearable Affective Robot 186
16.4 Algorithm for the Wearable Affective Robot 186
16.4.1 Algorithm for Affective Recognition 186
16.4.2 User-Behavior Perception based on a Brain-Wearable Device 186
16.5 Life Modeling of the Wearable Affective Robot 187
16.5.1 Data Set Labeling and Processing 188
16.5.2 Multidimensional Data Integration 188
16.5.3 Modeling of Associated Scenarios 188
16.6 Challenges and Prospects 189
16.6.1 Research Challenges of the Wearable Affective Robot 189
16.6.2 Application Scenarios for the Wearable Affective Robot 189
16.7 Conclusions 190
References 190
17 Visual Human–Computer Interactions for Intelligent Vehicles 193
Xumeng Wang, Wei Chen, and Fei-Yue Wang
17.1 Introduction 193
17.2 Background 193
17.3 State-of-the-Art 194
17.3.1 VHCI in Vehicles 194
17.3.1.1 Information Feedback from Intelligent Vehicles 195
17.3.1.2 Human-Guided Driving 195
17.3.2 VHCI Among Vehicles 195
17.3.3 VHCI Beyond Vehicles 195
17.4 Future Research Challenges 196
17.4.1 VHCI for Intelligent Vehicles 196
17.4.1.1 Vehicle Development 196
17.4.1.2 Vehicle Manufacture 197
17.4.1.3 Preference Recording 197
17.4.1.4 Vehicle Usage 197
17.4.2 VHCI for Intelligent Transportation Systems 198
17.4.2.1 Parallel World 198
17.4.2.2 The Framework of Intelligent Transportation Systems 198
References 199
18 Intelligent Collaboration Between Humans and Robots 203
Andrea Maria Zanchettin
18.1 Introduction 203
18.2 Background 203
18.2.1 Context 203
18.2.2 Basic Definitions 204
18.3 Related Work 205
18.4 Validation Cases 206
18.4.1 A Simple Verification Scenario 207
18.4.2 Activity Recognition Based on Semantic Hand-Object Interaction 208
18.5 Conclusions 210
18.6 Future Research Challenges 210
References 210
19 To Be Trustworthy and To Trust: The New Frontier of Intelligent Systems 213
Rino Falcone, Alessandro Sapienza, Filippo Cantucci, and Cristiano Castelfranchi
19.1 Introduction 213
19.2 Background 214
19.3 Basic Definitions 214
19.4 State-of-the-Art 215
19.4.1 Trust in Different Domains 215
19.4.2 Selected Articles 215
19.4.3 Differences in the Use of Trust 216
19.4.4 Approaches to Model Trust 217
19.4.5 Sources of Trust 218
19.4.6 Different Computational Models of Trust 218
19.5 Future Research Challenges 220
References 221
20 Decoding Humans’ and Virtual Agents’ Emotional Expressions 225
Terry Amorese, Gennaro Cordasco, Marialucia Cuciniello, Olga Shevaleva, Stefano Marrone, Carl Vogel, and Anna Esposito
20.1 Introduction 225
20.2 Related Work 226
20.3 Materials and Methodology 227
20.3.1 Participants 227
20.3.2 Stimuli 228
20.3.3 Tools and Procedures 228
20.4 Descriptive Statistics 229
20.5 Data Analysis and Results 230
20.5.1 Comparison Synthetic vs. Naturalistic Experiment 234
20.6 Discussion and Conclusions 235
Acknowledgment 238
References 238
21 Intelligent Computational Edge: From Pervasive Computing and Internet of Things to Computing Continuum 241
Radmila Juric
21.1 Introduction 241
21.2 The Journey of Pervasive Computing 242
21.3 The Power of the IoT 243
21.3.1 Inherent Problems with the IoT 244
21.4 IoT: The Journey from Cloud to Edge 245
21.5 Toward Intelligent Computational Edge 246
21.6 Is Computing Continuum the Answer? 247
21.7 Do We Have More Questions than Answers? 248
21.8 What Would our Vision Be? 249
References 251
22 Implementing Context Awareness in Autonomous Vehicles 257
Federico Faruffini, Alessandro Correa-Victorino, and Marie-Hélène Abel
22.1 Introduction 257
22.2 Background 258
22.2.1 Ontologies 258
22.2.2 Autonomous Driving 258
22.2.3 Basic Definitions 259
22.3 Related Works 260
22.4 Implementation and Tests 261
22.4.1 Implementing the Context of Navigation 261
22.4.2 Control Loop Rule 262
22.4.3 Simulations 263
22.5 Conclusions 264
22.6 Future Research Challenges 264
References 264
23 The Augmented Workforce: A Systematic Review of Operator Assistance Systems 267
Elisa Roth, Mirco Moencks, and Thomas Bohné
23.1 Introduction 267
23.2 Background 268
23.2.1 Definitions 268
23.3 State of the Art 269
23.3.1 Empirical Considerations 270
23.3.1.1 Application Areas 270
23.3.2 Assistance Capabilities 270
23.3.2.1 Task Guidance 271
23.3.2.2 Knowledge Management 271
23.3.2.3 Monitoring 273
23.3.2.4 Communication 273
23.3.2.5 Decision-Making 273
23.3.3 Meta-capabilities 274
23.3.3.1 Configuration Flexibility 274
23.3.3.2 Interoperability 274
23.3.3.3 Content Authoring 274
23.3.3.4 Initiation 274
23.3.3.5 Hardware 275
23.3.3.6 User Interfaces 275
23.4 Future Research Directions 275
23.4.1 Empirical Evidence 275
23.4.2 Collaborative Research 277
23.4.3 Systemic Approaches 277
23.4.4 Technology-Mediated Learning 277
23.5 Conclusion 277
References 278
24 Cognitive Performance Modeling 281
Maryam Zahabi and Junho Park
24.1 Introduction 281
24.2 Background 281
24.3 State-of-the-Art 282
24.4 Current Research Issues 286
24.5 Future Research Directions Dealing with the Current Issues 286
References 287
25 Advanced Driver Assistance Systems: Transparency and Driver Performance Effects 291
Yulin Deng and David B. Kaber
25.1 Introduction 291
25.2 Background 292
25.2.1 Context 292
25.2.2 Basic Definition 292
25.3 Related Work 293
25.4 Method 294
25.4.1 Apparatus 295
25.4.2 Participants 296
25.4.3 Experiment Design 296
25.4.4 Tasks 297
25.4.5 Dependent Variables 297
25.4.5.1 Hazard Negotiation Performance 297
25.4.5.2 Vehicle Control Performance 298
25.4.6 Procedure 298
25.5 Results 299
25.5.1 Hazard Reaction Performance 299
25.5.2 Posthazard Manual Driving Performance 299
25.5.3 Posttesting Usability Questionnaire 301
25.6 Discussion 302
25.7 Conclusion 303
25.8 Future Research 304
References 304
26 RGB-D Based Human Action Recognition: From Handcrafted to Deep Learning 307
Bangli Liu and Honghai Liu
26.1 Introduction 307
26.2 RGB-D Sensors and 3D Data 307
26.3 Human Action Recognition via Handcrafted Methods 308
26.3.1 Skeleton-Based Methods 308
26.3.2 Depth-Based Methods 309
26.3.3 Hybrid Feature-Based Methods 309
26.4 Human Action Recognition via Deep Learning Methods 310
26.4.1 CNN-Based Methods 310
26.4.2 RNN-Based Methods 311
26.4.3 GCN-Based Methods 313
26.5 Discussion 314
26.6 RGB-D Datasets 314
26.7 Conclusion and Future Directions 315
References 316
27 Hybrid Intelligence: Augmenting Employees’ Decision-Making with AI-Based Applications 321
Ina Heine, Thomas Hellebrandt, Louis Huebser, and Marcos Padrón
27.1 Introduction 321
27.2 Background 321
27.2.1 Context 321
27.2.2 Basic Definitions 322
27.3 Related Work 323
27.4 Technical Part of the Chapter 324
27.4.1 Description of the Use Case 324
27.4.1.1 Business Model 324
27.4.1.2 Process 324
27.4.1.3 Use Case Objectives 325
27.4.2 Description of the Envisioned Solution 325
27.4.3 Development Approach of AI Application 326
27.4.3.1 Development Process 326
27.4.3.2 Process Analysis and Time Study 326
27.4.3.3 Development and Deployment Data 327
27.4.3.4 System Testing and Deployment 327
27.4.3.5 Development Infrastructure and Development Cost Monitoring 327
27.5 Conclusions 330
27.6 Future Research Challenges 330
References 330
28 Human Factors in Driving 333
Birsen Donmez, Dengbo He, and Holland M. Vasquez
28.1 Introduction 333
28.2 Research Methodologies 334
28.3 In-Vehicle Electronic Devices 335
28.3.1 Distraction 335
28.3.2 Interaction Modality 336
28.3.2.1 Visual and Manual Modalities 336
28.3.2.2 Auditory and Vocal Modalities 337
28.3.2.3 Haptic Modality 338
28.3.3 Wearable Devices 338
28.4 Vehicle Automation 339
28.4.1 Driver Support Features 339
28.4.2 Automated Driving Features 341
28.5 Driver Monitoring Systems 342
28.6 Conclusion 343
References 343
29 Wearable Computing Systems: State-of-the-Art and Research Challenges 349
Giancarlo Fortino and Raffaele Gravina
29.1 Introduction 349
29.2 Wearable Devices 350
29.2.1 A History of Wearables 350
29.2.2 Sensor Types 351
29.2.2.1 Physiological Sensors 352
29.2.2.2 Inertial Sensors 352
29.2.2.3 Visual Sensors 352
29.2.2.4 Audio Sensors 355
29.2.2.5 Other Sensors 355
29.3 Body Sensor Networks-based Wearable Computing Systems 355
29.3.1 Body Sensor Networks 355
29.3.2 The SPINE Body-of-Knowledge 357
29.3.2.1 The SPINE Framework 357
29.3.2.2 The BodyCloud Framework 359
29.4 Applications of Wearable Devices and BSNs 360
29.4.1 Healthcare 360
29.4.1.1 Cardiovascular Disease 362
29.4.1.2 Parkinson’s Disease 362
29.4.1.3 Respiratory Disease 362
29.4.1.4 Diabetes 363
29.4.1.5 Rehabilitation 363
29.4.2 Fitness 363
29.4.2.1 Diet Monitoring 363
29.4.2.2 Activity/Fitness Tracker 363
29.4.3 Sports 364
29.4.4 Entertainment 364
29.5 Challenges and Prospects 364
29.5.1 Materials and Wearability 364
29.5.2 Power Supply 365
29.5.3 Security and Privacy 365
29.5.4 Communication 365
29.5.5 Embedded Computing, Development Methodologies, and Edge AI 365
29.6 Conclusions 365
Acknowledgment 366
References 366
30 Multisensor Wearable Device for Monitoring Vital Signs and Physical Activity 373
Joshua Di Tocco, Luigi Raiano, Daniela lo Presti, Carlo Massaroni, Domenico Formica, and Emiliano Schena
30.1 Introduction 373
30.2 Background 373
30.2.1 Context 373
30.2.2 Basic Definitions 374
30.3 Related Work 375
30.4 Case Study: Multisensor Wearable Device for Monitoring RR and Physical Activity 376
30.4.1 Wearable Device Description 376
30.4.1.1 Module for the Estimation of RR 377
30.4.1.2 Module for the Estimation of Physical Activity 377
30.4.2 Experimental Setup and Protocol 378
30.4.2.1 Experimental Setup 378
30.4.2.2 Experimental Protocol 378
30.4.3 Data Analysis 378
30.4.4 Results 378
30.5 Conclusions 379
30.6 Future Research Challenges 380
References 380
31 Integration of Machine Learning with Wearable Technologies 383
Darius Nahavandi, Roohallah Alizadehsani, and Abbas Khosravi
31.1 Introduction 383
31.2 Background 384
31.2.1 History of Wearables 384
31.2.2 Supervised Learning 384
31.2.3 Unsupervised Learning 386
31.2.4 Deep Learning 386
31.2.5 Deep Deterministic Policy Gradient 387
31.2.6 Cloud Computing 388
31.2.7 Edge Computing 388
31.3 State of the Art 389
31.4 Future Research Challenges 392
References 393
32 Gesture-Based Computing 397
Gennaro Costagliola, Mattia De Rosa, and Vittorio Fuccella
32.1 Introduction 397
32.2 Background 398
32.2.1 History of the Development of Gesture-Based Computing 398
32.2.2 Basic Definitions 399
32.3 State of the Art 399
32.4 Future Research Challenges 402
32.4.1 Current Research Issues 402
32.4.2 Future Research Directions Dealing with the Current Issues 403
Acknowledgment 403
References 403
33 EEG-based Affective Computing 409
Xueliang Quan and Dongrui Wu
33.1 Introduction 409
33.2 Background 409
33.2.1 Brief History 409
33.2.2 Emotion Theory 410
33.2.3 Emotion Representation 410
33.2.4 Eeg 410
33.2.5 EEG-Based Emotion Recognition 411
33.3 State-of-the-Art 411
33.3.1 Public Datasets 411
33.3.2 EEG Feature Extraction 411
33.3.3 Feature Fusion 412
33.3.4 Affective Computing Algorithms 413
33.3.4.1 Transfer Learning 413
33.3.4.2 Active Learning 413
33.3.4.3 Deep Learning 413
33.4 Challenges and Future Directions 414
Acknowledgment 415
References 415
34 Security of Human Machine Systems 419
Francesco Flammini, Emanuele Bellini, Maria Stella de Biase, and Stefano Marrone
34.1 Introduction 419
34.2 Background 420
34.2.1 An Historical Retrospective 420
34.2.2 Foundations of Security Theory 421
34.2.3 A Reference Model 421
34.3 State of the Art 422
34.3.1 Survey Methodology 422
34.3.2 Research Trends 425
34.4 Conclusions and Future Research 426
References 428
35 Integrating Innovation: The Role of Standards in Promoting Responsible Development of Human–Machine Systems 431
Zach McKinney, Martijn de Neeling, Luigi Bianchi, and Ricardo Chavarriaga
35.1 Introduction to Standards in Human–Machine Systems 431
35.1.1 What Are Standards? 431
35.1.2 Standards in Context: Technology Governance, Best Practice, and Soft Law 432
35.1.3 The Need for Standards in HMS 433
35.1.4 Benefits of Standards 433
35.1.5 What Makes an Effective Standard? 434
35.2 The HMS Standards Landscape 435
35.2.1 Standards in Neuroscience and Neurotechnology for Brain–Machine Interfaces 435
35.2.2 IEEE P2731 – Unified Terminology for BCI 435
35.2.2.1 The BCI Glossary 439
35.2.2.2 The BCI Functional Model 439
35.2.2.3 BCI Data Storage 439
35.2.3 IEEE P2794 – Reporting Standard for in vivo Neural Interface Research (RSNIR) 441
35.3 Standards Development Process 443
35.3.1 Who Can Participate in Standards Development? 443
35.3.2 Why Should I Participate in Standards Development? 444
35.3.3 How Can I get Involved in Standards Development? 444
35.4 Strategic Considerations and Discussion 444
35.4.1 Challenges to Development and Barriers to Adoption of Standards 444
35.4.2 Strategies to Promote Standards Development and Adoption 445
35.4.3 Final Perspective: On Innovation 445
Acknowledgements 446
References 446
36 Situation Awareness in Human-Machine Systems 451
Giuseppe D’Aniello and Matteo Gaeta
36.1 Introduction 451
36.2 Background 452
36.3 State-of-the-Art 453
36.3.1 Situation Identification Techniques in HMS 454
36.3.2 Situation Evolution in HMS 455
36.3.3 Situation-Aware Human Machine-Systems 455
36.4 Discussion and Research Challenges 456
36.5 Conclusion 458
References 458
37 Modeling, Analyzing, and Fostering the Adoption of New Technologies: The Case of Electric Vehicles 463
Valentina Breschi, Chiara Ravazzi, Silvia Strada, Fabrizio Dabbene, and Mara Tanelli
37.1 Introduction 463
37.2 Background 464
37.2.1 An Agent-based Model for EV Transition 464
37.2.2 Calibration Based on Real Mobility Patterns 466
37.3 Fostering the EV Transition via Control over Networks 468
37.3.1 Related Work: A Perspective Analysis 468
37.3.2 A New Model for EV Transition with Incentive Policies 469
37.3.2.1 Modeling Time-varying Thresholds 469
37.3.2.2 Calibration of the Model 470
37.4 Boosting EV Adoption with Feedback 470
37.4.1 Formulation of the Optimal Control Problem 470
37.4.2 Derivation of the Optimal Policies 471
37.4.3 A Receding Horizon Strategy to Boost EV Adoption 472
37.5 Experimental Results 473
37.6 Conclusions 476
37.7 Future Research Challenges 477
Acknowlegments 477
References 477
Index 479
