Inhalt

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   Contents
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  Introduction
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   Motivation and Scope
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   Research Questions
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   Interdisciplinary Background and Related Work
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  Interdisciplinary Background
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   Working Definitions of Affective Phenomena
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   Emotion Models
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   Stress Models
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  Physiological Changes and Objective Measures
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   Affective States and their Physiological Indicators
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   Frequently Employed Sensors
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  Affect-related User Studies
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   Affect-related User Studies in Laboratory Settings
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   Affect-related User Studies in the Field
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   Publicly Available Datasets
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  Data Processing and Classification
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   Preprocessing and Segmentation
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   Physiological Feature Extraction
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   Classification
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   Conclusion
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   Study I: Wearable-based Affect Recognition in the Lab
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   Related Work
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   Lab Study Protocol, Self-reports, and Sensors
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   Evaluation of Self-reports and Saliva Samples
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  Employed Sensors and Feature-based Evaluation
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   Feature Extraction
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   Classification Algorithms and Evaluation Metric
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   Classification Results
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   Conclusion
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   Study II: Wearable-based Affect Recognition in the Field
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   Related Work
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   Field Study Protocol
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   EMA Guidelines, Implementation Details, and Lessons Learned
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   Discussion
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  Quantitative Analysis of the Field Study Data
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   Considered Data and Label
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   Evaluation Method and Metric
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   Classification Algorithms
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   Classification Results
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  Limitations and Further Considerations
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   Limitations of the Presented Approach
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   Inherent Pitfalls of Affect Recognition in the Wild
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   Conclusion
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  Résumé
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   Results and Contributions
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   Future Work
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   Appendices
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  Lists
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   List of Figures
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   List of Tables
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   Acknowledgements
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   Publication List
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   Glossary
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   Bibliography