Handbook 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, and various aspects of interactive and wearable computers and systems. The book also includes:

• A thorough introduction to human-machine systems via the use of emblematic use cases, as well as discussions of potential future research challenges
• Comprehensive explorations of hybrid technologies, which focus on transversal aspects of human-machine systems
• Practical discussions of human-machine cooperation principles and methods for the design and evaluation of a brain-computer interface

Perfect for academic and technical researchers with an interest in HMS, Handbook of Human-Machine Systems will also earn a place in the libraries of technical professionals practicing in areas including computer science, artificial intelligence, cognitive science, engineering, psychology, and neurobiology.

Giancarlo Fortino, PhD, is a Full Professor of Computer Engineering, Chair of the ICT PhD School, and Rector's Delegate for International Relations with the Department of Informatics, Modeling, Electronics, and Systems at University of Calabria, Italy.

David Kaber, PhD, is the Department Chair and Dean's Leadership Professor with the Department of Industrial & Systems Engineering at the University of Florida.

Andreas Nürnberger, PhD, is a Full Professor for Data and Knowledge Engineering in the Faculty of Computer Science at Otto-von-Guericke-Universität Magdeburg, Germany.

David Mendonca, PhD, is a Full Professor with the Department of Industrial and Systems Engineering at Rensselaer Polytechnic Institute.

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

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