Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Measuring emotional valence during interactive experiences: boys at video game play
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
International Journal of Human-Computer Studies
Automatic prediction of frustration
International Journal of Human-Computer Studies
Entertainment capture through heart rate activity in physical interactive playgrounds
User Modeling and User-Adapted Interaction
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
Entertainment modeling through physiology in physical play
International Journal of Human-Computer Studies
Early Prediction of Student Frustration
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Flow and immersion in first-person shooters: measuring the player's gameplay experience
Future Play '08 Proceedings of the 2008 Conference on Future Play: Research, Play, Share
Affective game engines: motivation and requirements
Proceedings of the 4th International Conference on Foundations of Digital Games
Modeling User Affect from Causes and Effects
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Towards affective camera control in games
User Modeling and User-Adapted Interaction
Enjoyment recognition from physiological data in a car racing game
Proceedings of the 3rd international workshop on Affective interaction in natural environments
Genetic search feature selection for affective modeling: a case study on reported preferences
Proceedings of the 3rd international workshop on Affective interaction in natural environments
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A common practice in modeling affect from physiological signals consists of reducing the signals to a set of statistical features that feed predictors of self-reported emotions. This paper analyses the impact of various timewindows, used for the extraction of physiological features, to the accuracy of affective models of players in a simple 3D game. Results show that the signals recorded in the central part of a short gaming experience contain more relevant information to the prediction of positive affective states than the starting and ending parts while the relevant information to predict anxiety and frustration appear not to be localized in a specific time interval but rather dependent on particular game stimuli.