5365 articles – 2584 references  [version française]
Short view
ASSESSING GAMEPLAY EMOTIONS FROM PHYSIOLOGICAL SIGNALS: A FUZZY DECISION TREES BASED MODEL
Orero, J. O., Levillain, F., Damez-Fontaine, M., Rifqi, M., Bouchon-Meunier, B.
in INTERNATIONAL CONFERENCE ON KANSEI ENGINEERING AND EMOTION RESEARCH 2010 - INTERNATIONAL CONFERENCE ON KANSEI ENGINEERING AND EMOTION RESEARCH 2010, Paris : France (2010) - http://hal.archives-ouvertes.fr/hal-00589941
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Conference proceedings
Joseph Onderi Orero ()1, Florent Levillain2, Marc Damez-Fontaine1, Maria Rifqi1, Bernadette Bouchon-Meunier1
1:  LIP6 – Laboratoire d'Informatique de Paris 6
http://www.lip6.fr/
CNRS : UMR7606 – Université Pierre et Marie Curie [UPMC] - Paris VI
4 Place JUSSIEU 75252 PARIS CEDEX 05
France
2:  CHART-LUTIN – Laboratoire des Usages en Technologies d'Information Numériques
UTC – CITE SCIENCES IND – Université Paris VIII - Vincennes Saint-Denis : EA4004
75930 PARIS CEDEX 19
France
Computer Science/Technology for Human Learning
Humanities and Social Sciences/Education
ASSESSING GAMEPLAY EMOTIONS FROM PHYSIOLOGICAL SIGNALS: A FUZZY DECISION TREES BASED MODEL
As 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
English
2010-03-01

INTERNATIONAL CONFERENCE ON KANSEI ENGINEERING AND EMOTION RESEARCH 2010
international
2010-03-05
1684-1693

INTERNATIONAL CONFERENCE ON KANSEI ENGINEERING AND EMOTION RESEARCH 2010
2010-03-02
2010-03-04
Paris
France

Emotion Recognition – Video Games – Physiological Signals – Fuzzy Sets.
Cognitive psychology