Assessing Gameplay Emotions from physiological signals: a fuzzy decision trees based model

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dc.creatorOrero, Joseph Onderi
dc.creatorLevillain, Florent
dc.creatorDamez-Fontaine, Marc
dc.creatorRifqi, Maria
dc.creatorBouchon-Meunier, Bernadette
dc.date02/13/2014
dc.dateThu, 13 Feb 2014
dc.dateThu, 13 Feb 2014 15:21:03
dc.dateThu, 13 Feb 2014 15:21:03
dc.date.accessioned2015-03-18T11:29:12Z
dc.date.available2015-03-18T11:29:12Z
dc.descriptionPaper presented at INTERNATIONAL CONFERENCE ON KANSEI ENGINEERING AND EMOTION RESEARCH 2010, KEER2010, PARIS | MARCH 2-4 2010
dc.descriptionAs video games become a widespread form of entertainment, there is need to develop new evaluative methodologies for acknowledging the various aspects of the player’s subjective experience, and especially the emotional aspect. Video game developers could benefit from being aware of how the player reacts emotionally to specific game parameters. In this study, we addressed the possibility to record physiological measures on players involved in an action game, with the main objective of developing adequate models to describe emotional states. Our goal was to estimate the emotional state of the player from physiological signals so as to relate these variations of the autonomic nervous system to the specific game narratives. To achieve this, we developed a fuzzy set theory based model to recognize various episodes of the game from the user’s physiological signals. We used fuzzy decision trees to generate the rules that map these signals to game episodes characterized by a variation of challenge at stake. A specific advantage to our approach is that we automatically recognize game episodes from physiological signals with explicitly defined rules relating the signals to episodes in a continuous scale. We compare our results with the actual game statistics information associated with the game episodes.
dc.description.abstractAs video games become a widespread form of entertainment, there is need to develop new evaluative methodologies for acknowledging the various aspects of the player’s subjective experience, and especially the emotional aspect. Video game developers could benefit from being aware of how the player reacts emotionally to specific game parameters. In this study, we addressed the possibility to record physiological measures on players involved in an action game, with the main objective of developing adequate models to describe emotional states. Our goal was to estimate the emotional state of the player from physiological signals so as to relate these variations of the autonomic nervous system to the specific game narratives. To achieve this, we developed a fuzzy set theory based model to recognize various episodes of the game from the user’s physiological signals. We used fuzzy decision trees to generate the rules that map these signals to game episodes characterized by a variation of challenge at stake. A specific advantage to our approach is that we automatically recognize game episodes from physiological signals with explicitly defined rules relating the signals to episodes in a continuous scale. We compare our results with the actual game statistics information associated with the game episodes.
dc.identifier.urihttp://hdl.handle.net/11071/3768
dc.languageeng
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dc.subjectEmotion Recognition
dc.subjectVideo Games
dc.subjectPhysiological Signals
dc.subjectFuzzy Sets
dc.titleAssessing Gameplay Emotions from physiological signals: a fuzzy decision trees based model
dc.typeConference Paper
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