Handbook of Human-Machine Systems

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 Mendonça, PhD, is a Senior Principal Decision Scientist at Advanced Software Innovation.

• Editors Biography xxiList of Contributors xxiiiPreface xxxiii1 Introduction 1Giancarlo Fortino, David Kaber, Andreas Nürnberger, and David Mendonça1.1 Book Rationale 11.2 Chapters Overview 2Acknowledgments 8References 82 Brain–Computer Interfaces: Recent Advances, Challenges, and Future Directions 11Tiago H. Falk, Christoph Guger, and Ivan Volosyak2.1 Introduction 112.2 Background 122.2.1 Active/Reactive BCIs 132.2.2 Passive BCIs 142.2.3 Hybrid BCIs 152.3 Recent Advances and Applications 152.3.1 Active/Reactive BCIs 152.3.2 Passive BCIs 162.3.3 Hybrid BCIs 162.4 Future Research Challenges 162.4.1 Current Research Issues 172.4.2 Future Research Directions 172.5 Conclusions 18References 183 Brain–Computer Interfaces for Affective Neurofeedback Applications 23Lucas R. Trambaiolli and Tiago H. Falk3.1 Introduction 233.2 Background 233.3 State-of-the-Art 243.3.1 Depressive Disorder 253.3.2 Posttraumatic Stress Disorder, PTSD 263.4 Future Research Challenges 273.4.1 Open Challenges 273.4.2 Future Directions 283.5 Conclusion 28References 294 Pediatric Brain–Computer Interfaces: An Unmet Need 35Eli 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 Kirton4.1 Introduction 354.1.1 Motivation 364.2 Background 364.2.1 Components of a BCI 364.2.1.1 Signal Acquisition 364.2.1.2 Signal Processing 364.2.1.3 Feedback 364.2.1.4 Paradigms 374.2.2 Brain Anatomy and Physiology 374.2.3 Developmental Neurophysiology 384.2.4 Clinical Translation of BCI 384.2.4.1 Assistive Technology (AT) 384.2.4.2 Clinical Assessment 394.3 Current Body of Knowledge 394.4 Considerations for Pediatric BCI 404.4.1 Developmental Impact on EEG-based BCI 404.4.2 Hardware for Pediatric BCI 414.4.3 Signal Processing for Pediatric BCI 414.4.3.1 Feature Extraction, Selection and Classification 424.4.3.2 Emerging Techniques 424.4.4 Designing Experiments for Pediatric BCI 434.4.5 Meaningful Applications for Pediatric BCI 434.4.6 Clinical Translation of Pediatric BCI 444.5 Conclusions 44References 455 Brain–Computer Interface-based Predator–Prey Drone Interactions 49Abdelkader Nasreddine Belkacem and Abderrahmane Lakas5.1 Introduction 495.2 Related Work 505.3 Predator–Prey Drone Interaction 515.4 Conclusion and Future Challenges 57References 586 Levels of Cooperation in Human–Machine Systems: A Human–BCI–Robot Example 61Marie-Pierre Pacaux-Lemoine, Lydia Habib, and Tom Carlson6.1 Introduction 616.2 Levels of Cooperation 616.3 Application to the Control of a Robot by Thought 636.3.1 Designing the System 646.3.2 Experiments and Results 666.4 Results from the Methodological Point of View 676.5 Conclusion and Perspectives 68References 697 Human–Machine Social Systems: Test and Validation via Military Use Cases 71Charlene K. Stokes, Monika Lohani, Arwen H. DeCostanza, and Elliot Loh7.1 Introduction 717.2 Background Summary: From Tools to Teammates 727.2.1 Two Sides of the Equation 727.2.2 Moving Beyond the Cognitive Revolution 737.2.2.1 A Rediscovery of the Unconscious 747.3 Future Research Directions 757.3.1 Machine: Functional Designs 757.3.2 Human: Ground Truth 767.3.2.1 Physiological Computing 767.3.3 Context: Tying It All Together 777.3.3.1 Training and Team Models 777.4 Conclusion 79References 798 The Role of Multimodal Data for Modeling Communication in Artificial Social Agents 83Stephanie Gross and Brigitte Krenn8.1 Introduction 838.2 Background 848.2.1 Context 848.2.2 Basic Definitions 848.3 Related Work 848.3.1 HHI Data 858.3.2 HRI Data 858.3.2.1 Joint Attention and Robot Turn-Taking Capabilities 858.3.3 Public Availability of the Data 878.4 Datasets and Resulting Implications 878.4.1 Human Communicative Signals 878.4.1.1 Experimental Setup 878.4.1.2 Data Analysis and Results 888.4.2 Humans Reacting to Robot Signals 898.4.2.1 Comparing Different Robotic Turn-Giving Signals 898.4.2.2 Comparing Different Transparency Mechanisms 908.5 Conclusions 918.6 Future Research Challenges 91References 919 Modeling Interactions Happening in People-Driven Collaborative Processes 95Maximiliano Canche, Sergio F. Ochoa, Daniel Perovich, and Rodrigo Santos9.1 Introduction 959.2 Background 979.3 State-of-the-Art in Interaction Modeling Languages and Notations 989.3.1 Visual Languages and Notations 999.3.2 Comparison of Interaction Modeling Languages and Notations 1009.4 Challenges and Future Research Directions 101References 10210 Transparent Communications for Human–Machine Teaming 105JessieY.C.Chen10.1 Introduction 10510.2 Definitions and Frameworks 10510.3 Implementation of Transparent Human–Machine Interfaces in Intelligent Systems 10610.3.1 Human–Robot Interaction 10610.3.2 Multiagent Systems and Human–Swarm Interaction 10810.3.3 Automated/Autonomous Driving 10910.3.4 Explainable AI-Based Systems 10910.3.5 Guidelines and Assessment Methods 10910.4 Future Research Directions 110References 11111 Conversational Human–Machine Interfaces 115María Jesús Rodríguez-Sánchez, Kawtar Benghazi, David Griol, and Zoraida Callejas11.1 Introduction 11511.2 Background 11511.2.1 History of the Development of the Field 11611.2.2 Basic Definitions 11711.3 State-of-the-Art 11711.3.1 Discussion of the Most Important Scientific/Technical Contributions 11711.3.2 Comparison Table 11911.4 Future Research Challenges 12111.4.1 Current Research Issues 12111.4.2 Future Research Directions Dealing with the Current Issues 121References 12212 Interaction-Centered Design: An Enduring Strategy and Methodology for Sociotechnical Systems 125Ming Hou, Scott Fang, Wenbi Wang, and Philip S. E. Farrell12.1 Introduction 12512.2 Evolution of HMS Design Strategy 12612.2.1 A HMS Technology: Intelligent Adaptive System 12612.2.2 Evolution of IAS Design Strategy 12812.3 State-of-the-Art: Interaction-Centered Design 13012.3.1 A Generic Agent-based ICD Framework 13012.3.2 IMPACTS: An Human–Machine Teaming Trust Model 13212.3.3 ICD Roadmap for IAS Design and Development 13312.3.4 ICD Validation, Adoption, and Contributions 13412.4 IAS Design Challenges and Future Work 13512.4.1 Challenges of HMS Technology 13612.4.2 Future Work in IAS Design and Validation 136References 13713 Human–Machine Computing: Paradigm, Challenges, and Practices 141Zhiwen Yu, Qingyang Li, and Bin Guo13.1 Introduction 14113.2 Background 14213.2.1 History of the Development 14213.2.2 Basic Definitions 14313.3 State of the Art 14413.3.1 Technical Contributions 14413.3.2 Comparison Table 14813.4 Future Research Challenges 15013.4.1 Current Research Issues 15013.4.2 Future Research Directions 151References 15214 Companion Technology 155Andreas Wendemuth14.1 Introduction 15514.2 Background 15514.2.1 History 15614.2.2 Basic Definitions 15714.3 State-of-the-Art 15814.3.1 Discussion of the Most Important Scientific/Technical Contributions 15914.4 Future Research Challenges 15914.4.1 Current Research Issues 15914.4.2 Future Research Directions Dealing with the Current Issues 160References 16115 A Survey on Rollator-Type Mobility Assistance Robots 165Milad Geravand, Christian Werner, Klaus Hauer, and Angelika Peer15.1 Introduction 16515.2 Mobility Assistance Platforms 16515.2.1 Actuation 16615.2.2 Kinematics 16615.2.2.1 Locomotion Support 16615.2.2.2 STS Support 16615.2.3 Sensors 16815.2.4 Human–Machine Interfaces 16815.3 Functionalities 16815.3.1 STS Assistance 16915.3.2 Walking Assistance 16915.3.2.1 Maneuverability Improvement 16915.3.2.2 Gravity Compensation 17015.3.2.3 Obstacle Avoidance 17015.3.2.4 Falls Risk Prediction and Fall Prevention 17015.3.3 Localization and Navigation 17015.3.3.1 Map Building and Localization 17115.3.3.2 Path Planning 17115.3.3.3 Assisted Localization 17115.3.3.4 Assisted Navigation 17115.3.4 Further Functionalities 17115.3.4.1 Reminder Systems 17115.3.4.2 Health Monitoring 17115.3.4.3 Communication, Information, Entertainment, and Training 17215.4 Conclusion 172References 17316 A Wearable Affective Robot 181Jia Liu, Jinfeng Xu, Min Chen, and Iztok Humar16.1 Introduction 18116.2 Architecture Design and Characteristics 18316.2.1 Architecture of a Wearable Affective Robot 18316.2.2 Characteristics of a Wearable Affective Robot 18416.3 Design of the Wearable, Affective Robot’s Hardware 18516.3.1 AIWAC Box Hardware Design 18516.3.2 Hardware Design of the EEG Acquisition 18516.3.3 AIWAC Smart Tactile Device 18516.3.4 Prototype of the Wearable Affective Robot 18616.4 Algorithm for the Wearable Affective Robot 18616.4.1 Algorithm for Affective Recognition 18616.4.2 User-Behavior Perception based on a Brain-Wearable Device 18616.5 Life Modeling of the Wearable Affective Robot 18716.5.1 Data Set Labeling and Processing 18816.5.2 Multidimensional Data Integration 18816.5.3 Modeling of Associated Scenarios 18816.6 Challenges and Prospects 18916.6.1 Research Challenges of the Wearable Affective Robot 18916.6.2 Application Scenarios for the Wearable Affective Robot 18916.7 Conclusions 190References 19017 Visual Human–Computer Interactions for Intelligent Vehicles 193Xumeng Wang, Wei Chen, and Fei-Yue Wang17.1 Introduction 19317.2 Background 19317.3 State-of-the-Art 19417.3.1 VHCI in Vehicles 19417.3.1.1 Information Feedback from Intelligent Vehicles 19517.3.1.2 Human-Guided Driving 19517.3.2 VHCI Among Vehicles 19517.3.3 VHCI Beyond Vehicles 19517.4 Future Research Challenges 19617.4.1 VHCI for Intelligent Vehicles 19617.4.1.1 Vehicle Development 19617.4.1.2 Vehicle Manufacture 19717.4.1.3 Preference Recording 19717.4.1.4 Vehicle Usage 19717.4.2 VHCI for Intelligent Transportation Systems 19817.4.2.1 Parallel World 19817.4.2.2 The Framework of Intelligent Transportation Systems 198References 19918 Intelligent Collaboration Between Humans and Robots 203Andrea Maria Zanchettin18.1 Introduction 20318.2 Background 20318.2.1 Context 20318.2.2 Basic Definitions 20418.3 Related Work 20518.4 Validation Cases 20618.4.1 A Simple Verification Scenario 20718.4.2 Activity Recognition Based on Semantic Hand-Object Interaction 20818.5 Conclusions 21018.6 Future Research Challenges 210References 21019 To Be Trustworthy and To Trust: The New Frontier of Intelligent Systems 213Rino Falcone, Alessandro Sapienza, Filippo Cantucci, and Cristiano Castelfranchi19.1 Introduction 21319.2 Background 21419.3 Basic Definitions 21419.4 State-of-the-Art 21519.4.1 Trust in Different Domains 21519.4.2 Selected Articles 21519.4.3 Differences in the Use of Trust 21619.4.4 Approaches to Model Trust 21719.4.5 Sources of Trust 21819.4.6 Different Computational Models of Trust 21819.5 Future Research Challenges 220References 22120 Decoding Humans’ and Virtual Agents’ Emotional Expressions 225Terry Amorese, Gennaro Cordasco, Marialucia Cuciniello, Olga Shevaleva, Stefano Marrone, Carl Vogel, and Anna Esposito20.1 Introduction 22520.2 Related Work 22620.3 Materials and Methodology 22720.3.1 Participants 22720.3.2 Stimuli 22820.3.3 Tools and Procedures 22820.4 Descriptive Statistics 22920.5 Data Analysis and Results 23020.5.1 Comparison Synthetic vs. Naturalistic Experiment 23420.6 Discussion and Conclusions 235Acknowledgment 238References 23821 Intelligent Computational Edge: From Pervasive Computing and Internet of Things to Computing Continuum 241Radmila Juric21.1 Introduction 24121.2 The Journey of Pervasive Computing 24221.3 The Power of the IoT 24321.3.1 Inherent Problems with the IoT 24421.4 IoT: The Journey from Cloud to Edge 24521.5 Toward Intelligent Computational Edge 24621.6 Is Computing Continuum the Answer? 24721.7 Do We Have More Questions than Answers? 24821.8 What Would our Vision Be? 249References 25122 Implementing Context Awareness in Autonomous Vehicles 257Federico Faruffini, Alessandro Correa-Victorino, and Marie-Hélène Abel22.1 Introduction 25722.2 Background 25822.2.1 Ontologies 25822.2.2 Autonomous Driving 25822.2.3 Basic Definitions 25922.3 Related Works 26022.4 Implementation and Tests 26122.4.1 Implementing the Context of Navigation 26122.4.2 Control Loop Rule 26222.4.3 Simulations 26322.5 Conclusions 26422.6 Future Research Challenges 264References 26423 The Augmented Workforce: A Systematic Review of Operator Assistance Systems 267Elisa Roth, Mirco Moencks, and Thomas Bohné23.1 Introduction 26723.2 Background 26823.2.1 Definitions 26823.3 State of the Art 26923.3.1 Empirical Considerations 27023.3.1.1 Application Areas 27023.3.2 Assistance Capabilities 27023.3.2.1 Task Guidance 27123.3.2.2 Knowledge Management 27123.3.2.3 Monitoring 27323.3.2.4 Communication 27323.3.2.5 Decision-Making 27323.3.3 Meta-capabilities 27423.3.3.1 Configuration Flexibility 27423.3.3.2 Interoperability 27423.3.3.3 Content Authoring 27423.3.3.4 Initiation 27423.3.3.5 Hardware 27523.3.3.6 User Interfaces 27523.4 Future Research Directions 27523.4.1 Empirical Evidence 27523.4.2 Collaborative Research 27723.4.3 Systemic Approaches 27723.4.4 Technology-Mediated Learning 27723.5 Conclusion 277References 27824 Cognitive Performance Modeling 281Maryam Zahabi and Junho Park24.1 Introduction 28124.2 Background 28124.3 State-of-the-Art 28224.4 Current Research Issues 28624.5 Future Research Directions Dealing with the Current Issues 286References 28725 Advanced Driver Assistance Systems: Transparency and Driver Performance Effects 291Yulin Deng and David B. Kaber25.1 Introduction 29125.2 Background 29225.2.1 Context 29225.2.2 Basic Definition 29225.3 Related Work 29325.4 Method 29425.4.1 Apparatus 29525.4.2 Participants 29625.4.3 Experiment Design 29625.4.4 Tasks 29725.4.5 Dependent Variables 29725.4.5.1 Hazard Negotiation Performance 29725.4.5.2 Vehicle Control Performance 29825.4.6 Procedure 29825.5 Results 29925.5.1 Hazard Reaction Performance 29925.5.2 Posthazard Manual Driving Performance 29925.5.3 Posttesting Usability Questionnaire 30125.6 Discussion 30225.7 Conclusion 30325.8 Future Research 304References 30426 RGB-D Based Human Action Recognition: From Handcrafted to Deep Learning 307Bangli Liu and Honghai Liu26.1 Introduction 30726.2 RGB-D Sensors and 3D Data 30726.3 Human Action Recognition via Handcrafted Methods 30826.3.1 Skeleton-Based Methods 30826.3.2 Depth-Based Methods 30926.3.3 Hybrid Feature-Based Methods 30926.4 Human Action Recognition via Deep Learning Methods 31026.4.1 CNN-Based Methods 31026.4.2 RNN-Based Methods 31126.4.3 GCN-Based Methods 31326.5 Discussion 31426.6 RGB-D Datasets 31426.7 Conclusion and Future Directions 315References 31627 Hybrid Intelligence: Augmenting Employees’ Decision-Making with AI-Based Applications 321Ina Heine, Thomas Hellebrandt, Louis Huebser, and Marcos Padrón27.1 Introduction 32127.2 Background 32127.2.1 Context 32127.2.2 Basic Definitions 32227.3 Related Work 32327.4 Technical Part of the Chapter 32427.4.1 Description of the Use Case 32427.4.1.1 Business Model 32427.4.1.2 Process 32427.4.1.3 Use Case Objectives 32527.4.2 Description of the Envisioned Solution 32527.4.3 Development Approach of AI Application 32627.4.3.1 Development Process 32627.4.3.2 Process Analysis and Time Study 32627.4.3.3 Development and Deployment Data 32727.4.3.4 System Testing and Deployment 32727.4.3.5 Development Infrastructure and Development Cost Monitoring 32727.5 Conclusions 33027.6 Future Research Challenges 330References 33028 Human Factors in Driving 333Birsen Donmez, Dengbo He, and Holland M. Vasquez28.1 Introduction 33328.2 Research Methodologies 33428.3 In-Vehicle Electronic Devices 33528.3.1 Distraction 33528.3.2 Interaction Modality 33628.3.2.1 Visual and Manual Modalities 33628.3.2.2 Auditory and Vocal Modalities 33728.3.2.3 Haptic Modality 33828.3.3 Wearable Devices 33828.4 Vehicle Automation 33928.4.1 Driver Support Features 33928.4.2 Automated Driving Features 34128.5 Driver Monitoring Systems 34228.6 Conclusion 343References 34329 Wearable Computing Systems: State-of-the-Art and Research Challenges 349Giancarlo Fortino and Raffaele Gravina29.1 Introduction 34929.2 Wearable Devices 35029.2.1 A History of Wearables 35029.2.2 Sensor Types 35129.2.2.1 Physiological Sensors 35229.2.2.2 Inertial Sensors 35229.2.2.3 Visual Sensors 35229.2.2.4 Audio Sensors 35529.2.2.5 Other Sensors 35529.3 Body Sensor Networks-based Wearable Computing Systems 35529.3.1 Body Sensor Networks 35529.3.2 The SPINE Body-of-Knowledge 35729.3.2.1 The SPINE Framework 35729.3.2.2 The BodyCloud Framework 35929.4 Applications of Wearable Devices and BSNs 36029.4.1 Healthcare 36029.4.1.1 Cardiovascular Disease 36229.4.1.2 Parkinson’s Disease 36229.4.1.3 Respiratory Disease 36229.4.1.4 Diabetes 36329.4.1.5 Rehabilitation 36329.4.2 Fitness 36329.4.2.1 Diet Monitoring 36329.4.2.2 Activity/Fitness Tracker 36329.4.3 Sports 36429.4.4 Entertainment 36429.5 Challenges and Prospects 36429.5.1 Materials and Wearability 36429.5.2 Power Supply 36529.5.3 Security and Privacy 36529.5.4 Communication 36529.5.5 Embedded Computing, Development Methodologies, and Edge AI 36529.6 Conclusions 365Acknowledgment 366References 36630 Multisensor Wearable Device for Monitoring Vital Signs and Physical Activity 373Joshua Di Tocco, Luigi Raiano, Daniela lo Presti, Carlo Massaroni, Domenico Formica, and Emiliano Schena30.1 Introduction 37330.2 Background 37330.2.1 Context 37330.2.2 Basic Definitions 37430.3 Related Work 37530.4 Case Study: Multisensor Wearable Device for Monitoring RR and Physical Activity 37630.4.1 Wearable Device Description 37630.4.1.1 Module for the Estimation of RR 37730.4.1.2 Module for the Estimation of Physical Activity 37730.4.2 Experimental Setup and Protocol 37830.4.2.1 Experimental Setup 37830.4.2.2 Experimental Protocol 37830.4.3 Data Analysis 37830.4.4 Results 37830.5 Conclusions 37930.6 Future Research Challenges 380References 38031 Integration of Machine Learning with Wearable Technologies 383Darius Nahavandi, Roohallah Alizadehsani, and Abbas Khosravi31.1 Introduction 38331.2 Background 38431.2.1 History of Wearables 38431.2.2 Supervised Learning 38431.2.3 Unsupervised Learning 38631.2.4 Deep Learning 38631.2.5 Deep Deterministic Policy Gradient 38731.2.6 Cloud Computing 38831.2.7 Edge Computing 38831.3 State of the Art 38931.4 Future Research Challenges 392References 39332 Gesture-Based Computing 397Gennaro Costagliola, Mattia De Rosa, and Vittorio Fuccella32.1 Introduction 39732.2 Background 39832.2.1 History of the Development of Gesture-Based Computing 39832.2.2 Basic Definitions 39932.3 State of the Art 39932.4 Future Research Challenges 40232.4.1 Current Research Issues 40232.4.2 Future Research Directions Dealing with the Current Issues 403Acknowledgment 403References 40333 EEG-based Affective Computing 409Xueliang Quan and Dongrui Wu33.1 Introduction 40933.2 Background 40933.2.1 Brief History 40933.2.2 Emotion Theory 41033.2.3 Emotion Representation 41033.2.4 Eeg 41033.2.5 EEG-Based Emotion Recognition 41133.3 State-of-the-Art 41133.3.1 Public Datasets 41133.3.2 EEG Feature Extraction 41133.3.3 Feature Fusion 41233.3.4 Affective Computing Algorithms 41333.3.4.1 Transfer Learning 41333.3.4.2 Active Learning 41333.3.4.3 Deep Learning 41333.4 Challenges and Future Directions 414Acknowledgment 415References 41534 Security of Human Machine Systems 419Francesco Flammini, Emanuele Bellini, Maria Stella de Biase, and Stefano Marrone34.1 Introduction 41934.2 Background 42034.2.1 An Historical Retrospective 42034.2.2 Foundations of Security Theory 42134.2.3 A Reference Model 42134.3 State of the Art 42234.3.1 Survey Methodology 42234.3.2 Research Trends 42534.4 Conclusions and Future Research 426References 42835 Integrating Innovation: The Role of Standards in Promoting Responsible Development of Human–Machine Systems 431Zach McKinney, Martijn de Neeling, Luigi Bianchi, and Ricardo Chavarriaga35.1 Introduction to Standards in Human–Machine Systems 43135.1.1 What Are Standards? 43135.1.2 Standards in Context: Technology Governance, Best Practice, and Soft Law 43235.1.3 The Need for Standards in HMS 43335.1.4 Benefits of Standards 43335.1.5 What Makes an Effective Standard? 43435.2 The HMS Standards Landscape 43535.2.1 Standards in Neuroscience and Neurotechnology for Brain–Machine Interfaces 43535.2.2 IEEE P2731 – Unified Terminology for BCI 43535.2.2.1 The BCI Glossary 43935.2.2.2 The BCI Functional Model 43935.2.2.3 BCI Data Storage 43935.2.3 IEEE P2794 – Reporting Standard for in vivo Neural Interface Research (RSNIR) 44135.3 Standards Development Process 44335.3.1 Who Can Participate in Standards Development? 44335.3.2 Why Should I Participate in Standards Development? 44435.3.3 How Can I get Involved in Standards Development? 44435.4 Strategic Considerations and Discussion 44435.4.1 Challenges to Development and Barriers to Adoption of Standards 44435.4.2 Strategies to Promote Standards Development and Adoption 44535.4.3 Final Perspective: On Innovation 445Acknowledgements 446References 44636 Situation Awareness in Human-Machine Systems 451Giuseppe D’Aniello and Matteo Gaeta36.1 Introduction 45136.2 Background 45236.3 State-of-the-Art 45336.3.1 Situation Identification Techniques in HMS 45436.3.2 Situation Evolution in HMS 45536.3.3 Situation-Aware Human Machine-Systems 45536.4 Discussion and Research Challenges 45636.5 Conclusion 458References 45837 Modeling, Analyzing, and Fostering the Adoption of New Technologies: The Case of Electric Vehicles 463Valentina Breschi, Chiara Ravazzi, Silvia Strada, Fabrizio Dabbene, and Mara Tanelli37.1 Introduction 46337.2 Background 46437.2.1 An Agent-based Model for EV Transition 46437.2.2 Calibration Based on Real Mobility Patterns 46637.3 Fostering the EV Transition via Control over Networks 46837.3.1 Related Work: A Perspective Analysis 46837.3.2 A New Model for EV Transition with Incentive Policies 46937.3.2.1 Modeling Time-varying Thresholds 46937.3.2.2 Calibration of the Model 47037.4 Boosting EV Adoption with Feedback 47037.4.1 Formulation of the Optimal Control Problem 47037.4.2 Derivation of the Optimal Policies 47137.4.3 A Receding Horizon Strategy to Boost EV Adoption 47237.5 Experimental Results 47337.6 Conclusions 47637.7 Future Research Challenges 477Acknowlegments 477References 477Index 479