Affective computing
Empirical Evaluation of User Models and User-Adapted Systems
User Modeling and User-Adapted Interaction
Preface: Towards Adaptation of Interaction to Affective Factors
User Modeling and User-Adapted Interaction
Affective computing: challenges
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
MPTrain: a mobile, music and physiology-based personal trainer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Emotion representation and physiology assignments in digital systems
Interacting with Computers
How emotion is made and measured
International Journal of Human-Computer Studies
Entertainment capture through heart rate activity in physical interactive playgrounds
User Modeling and User-Adapted Interaction
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Introduction to special Issue on `Affective modeling and adaptation'
User Modeling and User-Adapted Interaction
Cluster Kernels: Resource-Aware Kernel Density Estimators over Streaming Data
IEEE Transactions on Knowledge and Data Engineering
User Modeling and User-Adapted Interaction
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Emotion Recognition Based on Physiological Changes in Music Listening
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of physiological computing
Interacting with Computers
A body-conforming tactile jacket to enrich movie viewing
WHC '09 Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Intimate Heartbeats: Opportunities for Affective Communication Technology
IEEE Transactions on Affective Computing
Selecting effective means to any end: futures and ethics of persuasion profiling
PERSUASIVE'10 Proceedings of the 5th international conference on Persuasive Technology
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Affective guidance of intelligent agents: How emotion controls cognition
Cognitive Systems Research
Brain computer interfaces as intelligent sensors for enhancing human-computer interaction
Proceedings of the 14th ACM international conference on Multimodal interaction
Cross-validation of bimodal health-related stress assessment
Personal and Ubiquitous Computing
Affective computing: a reverence for a century of research
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
AffectButton: A method for reliable and valid affective self-report
International Journal of Human-Computer Studies
How affective technologies can influence intimate interactions and improve social connectedness
International Journal of Human-Computer Studies
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The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners' personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application.