| Autore |
Tian Leimin
|
| Pubbl/distr/stampa |
San Rafael : , : Morgan & Claypool Publishers, , 2022
|
| Descrizione fisica |
1 online resource (308 pages)
|
| Disciplina |
004.19
|
| Altri autori (Persone) |
OviattSharon
MuszynskiMichal
|
| Soggetto topico |
Affect (Psychology)
|
| ISBN |
1-4503-9593-7
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| 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.
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| Record Nr. | UNINA-9910860810203321 |
Info
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