Entertainment modeling through physiology in physical play
International Journal of Human-Computer Studies
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ICEC '09 Proceedings of the 8th International Conference on Entertainment Computing
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Proceedings of the 2009 international conference on Multimodal interfaces
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Proceedings of the International Conference on Advances in Computer Enterntainment Technology
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User Modeling and User-Adapted Interaction
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ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Ranking vs. Preference: a comparative study of self-reporting
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Affective preference from physiology in videogames: a lesson learned from the TORCS experiment
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
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International Journal of Distance Education Technologies
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An approach for capturing and modeling individual entertainment ("fun") preferences is applied to users of the innovative Playware playground, an interactive physical playground inspired by computer games, in this study. The goal is to construct, using representative statistics computed from children's physiological signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose children's heart rate (HR) signals, and their expressed preferences of how much "fun" particular game variants are, are obtained from experiments using games implemented on the Playware playground. A comprehensive statistical analysis shows that children's reported entertainment preferences correlate well with specific features of the HR signal. Neuro-evolution techniques combined with feature set selection methods permit the construction of user models that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and two control tasks requiring physical activity. The best network is able to correctly match expressed preferences in 64% of cases on previously unseen data (p驴value 6 · 10驴5). The generality of the methodology, its limitations, its usability as a real-time feedback mechanism for entertainment augmentation and as a validation tool are discussed.