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

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 challengesComprehensive explorations of hybrid technologies, which focus on transversal aspects of human-machine systemsPractical 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.