Heart rate variability: indicator of user state as an aid to human-computer interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Measuring emotional valence during interactive experiences: boys at video game play
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Entertainment capture through heart rate activity in physical interactive playgrounds
User Modeling and User-Adapted Interaction
Capturing entertainment through heart rate dynamics in the playware playground
ICEC'06 Proceedings of the 5th international conference on Entertainment Computing
Towards capturing and enhancing entertainment in computer games
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
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An investigation into capturing the relation of physiology, beyond heart rate recording, to expressed preferences of entertainment in children's physical gameplay is presented in this paper. An exploratory survey experiment raises the difficulties of isolating elements derived (solely) from heart rate recordings attributed to reported entertainment and a control experiment for surmounting those difficulties is proposed. Then a survey experiment on a larger scale is devised where more physiological signals (Blood Volume Pulse and Skin Conductance) are collected and analyzed. Given effective data collection a set of numerical features is extracted from the child's physiological state. A preference learning mechanism based on neuro-evolution is used to construct a function of single physiological features that models the players' notion of `fun' for the games under investigation. Performance of the model is evaluated by the degree to which the preferences predicted by the model match those expressed by the children. Results indicate that there appears to be increased mental/emotional effort in preferred games of children.