Applied affective computing

• Autore: Tian Leimin
• Pubblicazione: San Rafael : , : Morgan & Claypool Publishers, , 2022; ©2022
• Descrizione fisica: 1 online resource (308 pages)
• Disciplina: 004.19
• Soggetto topico: Affect (Psychology)
• Altri autori: OviattSharon MuszynskiMichal
• Nota di contenuto: Intro -- Applied Affective Computing -- Contents -- List of Figures -- List of Tables -- Preface -- Acknowledgments -- 1 Introduction to Applied Affective Computing -- 1.1 Affective Computing -- 1.2 Applied Affective Computing -- 1.2.1 Challenges of Applied Affective Computing -- 1.2.1.1 Developing a Human-centered Affective Computing System -- 1.2.1.2 Developing a Reliable Affective Computing System -- 1.2.1.3 Developing an Adaptive Affective Computing System -- 1.2.1.4 Developing an Integrated Affective Computing System -- 1.2.1.5 Developing an Ethical Affective Computing System -- 1.2.2 A Living Ontology of Affective Computing -- 1.3 Book Overview and Highlights -- 1.4 Contributions -- 2 Emotions as Studied in Psychology and Cognitive Science -- 2.1 Theories of Emotion -- 2.1.1 The Evolutionary Approach -- 2.1.2 The Appraisal Approach -- 2.1.3 The Constructionism Approach -- 2.2 Emotion as an Adaptive Function -- 2.3 Emotion as a Social Communicative Function -- 2.3.1 Empathy -- 2.3.2 Social Intelligence and Socio-affective Competence -- 2.3.3 Social Norms -- 2.4 Summary -- 3 Machine Learning Approaches for Applied Affective Computing -- 3.1 Machine Learning for Affective Computing -- 3.2 Deep Machine Learning for Affective Computing -- 3.3 Multimodal Representation of Affect: Knowledge-inspired versus Data-driven Features -- 3.4 Unimodal Features for Affect Recognition -- 3.4.1 Visual Modality-Facial Expressions and Body Gestures -- 3.4.1.1 Facial Expression -- 3.4.1.2 Body Gestures -- 3.4.2 Deep Learning-based Visual Feature Extraction -- 3.4.3 Audio Modality -- 3.4.4 Deep Learning-based Audio Feature Extraction -- 3.4.5 Physiological Modality -- 3.4.6 Deep Learning-based Physiological Feature Extraction -- 3.4.7 Linguistic Modality -- 3.4.8 Deep Learning-based Linguistic Feature Extraction -- 3.5 Multimodal Emotion-aware Systems. ; 3.6 Evaluation of Emotion-aware Systems -- 3.7 Synthesis of Emotion and Emotional Behaviors -- 3.8 Discussion -- 3.8.1 Context Matters -- 3.8.2 Emotion Representation: Categories versus Dimensions -- 3.8.3 Perceived versus Felt Affect -- 3.9 Conclusion -- 4 Multimodal Data Collection and Processing for Applied Affective Computing -- 4.1 Multimodal Data Collection -- 4.1.1 Emotion Labels -- 4.1.2 Multimodal Data -- 4.1.2.1 Facial Expression -- 4.1.2.2 Speech -- 4.1.2.3 Body Movement -- 4.1.2.4 Text -- 4.1.2.5 Physiological Signals -- 4.1.2.6 Context Information -- 4.2 Multimodal Data Processing -- 4.2.1 Preprocessing -- 4.2.2 Feature Extraction -- 4.2.3 Data Reduction/Selection -- 4.2.4 Feature Normalization -- 4.3 Multimodal Data Fusion -- 4.3.1 Model-agnostic Approaches -- 4.3.1.1 Feature Level Fusion (Early Fusion) -- 4.3.1.2 Intermediate Fusion -- 4.3.1.3 Late Fusion (Decision Level Fusion) -- 4.3.2 Model-based Approaches -- 4.3.2.1 Kernel Learning -- 4.3.2.2 Graphical Models -- 4.3.2.3 Neural Networks -- 4.3.2.4 Multimodal Deep Autoencoders -- 4.4 Conclusion and Future Work -- 5 Emotion Recognition in the Wild -- 5.1 Modalities -- 5.1.1 Emotion Recognition from Audio and Visual Modality -- 5.1.2 Emotion Recognition from Physiological Signals -- 5.2 Deep Learning for Emotion Recognition in the Wild -- 5.3 Emotion Representation in the Wild -- 5.4 Evaluation of Affect Recognition in the Wild -- 5.5 The Role of Context in Affect Recognition in the Wild -- 5.6 The Role of Environment in Affect Recognition in the Wild -- 5.7 Discussion -- 5.7.1 Emotion Annotations: Self-report versus Third-person Annotations -- 5.7.2 Long-term Research on Affect in the Wild -- 5.7.3 Ethical and Privacy Issues of Affect Recognition in the Wild -- 5.8 Conclusion -- 6 Reinforcement Learning and Affective Computing -- 6.1 Reinforcement Learning. ; 6.1.1 Bandits and the Exploration-Exploitation Tradeoff -- 6.1.2 State Valuation -- 6.2 Affective Computing -- 6.2.1 Theories of Emotion -- 6.2.2 Behavioral Psychology -- 6.3 Extending the Reinforcement Learning Framework -- 6.3.1 Modeling Agent, Environment, and Critic -- 6.3.2 A Multilayered Affective Critic -- 6.3.3 Dynamic Goals and Rewards -- 6.3.4 Prioritization -- 6.4 Conclusions -- 7 Synthesizing Natural and Believable Emotional Expressions -- 7.1 Introduction -- 7.1.1 Historical Context -- 7.2 Emotional Expression Models -- 7.3 Synthesizing Emotional Expressions -- 7.4 The Role of Agency -- 7.5 Additional Considerations and Challenges -- 8 Emotion-aware Human-Robot Interaction and Social Robots -- 8.1 Developing Emotion-aware Human-Robot Interaction Systems -- 8.2 Improving Social Intelligence of a Robot by Addressing Social Errors in Human-Robot Interaction -- 8.3 Case Study: Prompting Human Assistance in Human-Robot Collaboration Using Robots' Emotional Expressions -- 8.3.1 Emotional Model of the Robot -- 8.3.2 Human-Multirobot Collaboration Task: The Tower Construction Game -- 8.3.3 Influence of Artificial Emotions on Human-Multirobot Collaboration -- 8.3.4 Validity and Expressiveness of the Robots' Emotional Expressions -- 8.3.5 Influence of Robots' Emotional Expressions on Human's Perception -- 8.3.6 Summary -- 8.4 Case Study: Understanding Users' Expectations of Robots in Public Spaces -- 8.4.1 Robots in Today's Public Spaces -- 8.4.2 Adopting Participatory Design to Understand Expectations and Perceptions of Robots in Public Spaces -- 8.4.3 Summary -- 8.5 Challenges in Affective HRI -- 8.6 Guidelines for Developing Affective HRI Systems and Social Robots -- 9 Affective Computing for Enhancing Well-Being -- 9.1 Motivation -- 9.2 Sensing and Analytics -- 9.3 Examples of Well-Being Studies -- 9.3.1 MIT Friends and Family. ; 9.3.2 MoodScope -- 9.3.3 Student Life -- 9.3.4 SNAPSHOT Study -- 9.3.5 Detecting Momentary Affective Changes in Daily Life -- 9.4 Sensing Beyond the Phone -- 9.4.1 Stationary Compute -- 9.4.2 Home Monitoring -- 9.4.3 In Vehicle -- 9.4.4 Cross-Platform Integration -- 9.5 Actions and Interventions -- 9.5.1 Conversing -- 9.5.2 Reminding and Recommending -- 9.5.3 Behavior Modification -- 9.5.4 Positive Computing -- 9.6 Conclusions -- 10 Applied Affective Computing in Built Environments -- 10.1 Setting the Foundation Toward Emotionally Aware Planning and Design -- 10.1.1 Design and Evaluation of Urban Spaces -- 10.1.2 Emotion and Perception -- 10.1.3 Sensors and Sensor Fusion -- 10.2 Case Study: Passive Responses to Urban Infrastructure -- 10.3 Experimental Task -- 10.4 Data -- 10.4.1 Human Subjects -- 10.4.2 Built Environment -- 10.5 Approach -- 10.5.1 Data Normalization -- 10.5.2 Machine Learning Models -- 10.6 Results -- 10.7 Conclusions -- 10.8 Guidelines for Affective Computing in Built Environments -- 11 Addressing Ethical Issues of Affective Computing -- 11.1 Ethical Concerns of Affective Computing -- 11.2 A Fair System -- 11.2.1 Biases in Data and Models -- 11.2.2 Increasing Fairness -- 11.3 A Privacy-preserving System -- 11.3.1 Issues of Invasive Surveillance -- 11.3.2 Privacy Preservation -- 11.4 A Transparent System -- 11.4.1 Overtrust Toward Intelligent Systems -- 11.4.2 Supporting Informed Decisions -- 11.5 A Beneficial System -- 11.5.1 Understanding Human Preferences -- 11.5.2 Contradicting Interests -- 11.6 A Responsible System -- 11.6.1 Accountability -- 11.6.2 Governance -- 11.7 Conclusion -- 12 Future of Affective Computing and Applied Affective Computing -- 12.1 Applied Affective Computing: Guidelines for Best Practice -- 12.2 Example Uses of the Guidelines -- 12.2.1 Scenario-based Discussion -- 12.2.1.1 Example A: Novice Researcher. ; 12.2.1.2 Example B: Experienced Researcher -- 12.2.1.3 Example C: Practitioner -- 12.2.2 Examples in Existing Studies -- 12.3 Open Challenges and Future Directions -- 12.3.1 Toward Rigorous Affective Computing Research Methodologies -- 12.3.1.1 Hypotheses Grounded in Emotion and Communication Theories -- 12.3.1.2 Ecological Validity -- 12.3.1.3 Evaluation with the Right Metrics -- 12.3.1.4 Reproducibility and Generalizability -- 12.3.2 Toward Personalized Affective Computing -- 12.3.3 Toward Adaptive Affective Computing -- 12.3.4 Toward Embodied Affective Computing -- 12.3.5 Application Domains of Interests -- 12.3.5.1 Healthcare -- 12.3.5.2 Education -- 12.3.5.3 Business -- 12.3.5.4 Design -- 12.3.5.5 Security -- 12.3.5.6 Entertainment -- 12.4 Summary -- Bibliography -- Authors' Biographies -- Index.
• Sommario/riassunto: This book offers readers an overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches to affective computing systems and social signal processing. It provides in-depth case studies of applied affective computing in various domains, such as social robotics and mental well-being. It also addresses ethical concerns related to affective computing and how to prevent misuse of the technology in research and applications. Further, this book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered.
• Titolo autorizzato: Applied affective computing
• ISBN: 1-4503-9593-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910860810203321
• Lo trovi qui: Univ. Federico II