The nature of statistical learning theory
The nature of statistical learning theory
Understanding and Using Context
Personal and Ubiquitous Computing
Emotion recognition from physiological signals using wireless sensors for presence technologies
Cognition, Technology and Work
Emotions and heart rate while sitting on a chair
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Emotion representation and physiology assignments in digital systems
Interacting with Computers
Consideration of Multiple Components of Emotions in Human-Technology Interaction
Affect and Emotion in Human-Computer Interaction
Auditory-Induced Emotion: A Neglected Channel for Communication in Human-Computer Interaction
Affect and Emotion in Human-Computer Interaction
Affect as a Mediator between Web-Store Design and Consumers' Attitudes toward the Store
Affect and Emotion in Human-Computer Interaction
In the Mood: Tagging Music with Affects
Affect and Emotion in Human-Computer Interaction
Using affective parameters in a content-based recommender system for images
User Modeling and User-Adapted Interaction
Affect detection from multichannel physiology during learning sessions with AutoTutor
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Towards emotional interaction: using movies to automatically learn users' emotional states
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part I
Affective modeling from multichannel physiology: analysis of day differences
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Perspectives on the evaluation of affective quality in social software
International Journal of Web Based Communities
Emoções na interação humano-computador: um estudo considerando sensores
Proceedings of the 12th Brazilian Symposium on Human Factors in Computing Systems
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This paper describes an experiment on emotion measurement and classification based on different physiological parameters, which was conducted in the context of a European project on ambient intelligent mobile devices. Emotion induction material consisted of five four-minute video films that induced two positive and three negative emotions. The experimental design gave consideration to both, the basic and the dimensional model of the structure of emotion. Statistical analyses were conducted for films and for self-assessed emotional state and in addition, supervised machine learning technique was utilized. Recognition rates reached up to 72% for a specific emotion (one out of five) and up to 82% for an underlying dimension (one out of two).