Show simple item record

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.identifier.urihttp://hdl.handle.net/11071/3768
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.languageeng
dc.rightsBy agreeing with and accepting this license, I (the author(s), copyright owner or nominated agent) agree to the conditions, as stated below, for deposit of the item (referred to as .the Work.) in the digital repository maintained by Strathmore University, or any other repository authorized for use by Strathmore University. Non-exclusive Rights Rights granted to the digital repository through this agreement are entirely non-exclusive. I understand that depositing the Work in the repository does not affect my rights to publish the Work elsewhere, either in present or future versions. I agree that Strathmore University may electronically store, copy or translate the Work to any approved medium or format for the purpose of future preservation and accessibility. Strathmore University is not under any obligation to reproduce or display the Work in the same formats or resolutions in which it was originally deposited. SU Digital Repository I understand that work deposited in the digital repository will be accessible to a wide variety of people and institutions, including automated agents and search engines via the World Wide Web. I understand that once the Work is deposited, metadata may be incorporated into public access catalogues. I agree as follows: 1.That I am the author or have the authority of the author/s to make this agreement and do hereby give Strathmore University the right to make the Work available in the way described above. 2.That I have exercised reasonable care to ensure that the Work is original, and to the best of my knowledge, does not breach any laws including those relating to defamation, libel and copyright. 3.That I have, in instances where the intellectual property of other authors or copyright holders is included in the Work, gained explicit permission for the inclusion of that material in the Work, and in the electronic form of the Work as accessed through the open access digital repository, or that I have identified that material for which adequate permission has not been obtained and which will be inaccessible via the digital repository. 4.That Strathmore University does not hold any obligation to take legal action on behalf of the Depositor, or other rights holders, in the event of a breach of intellectual property rights, or any other right, in the material deposited. 5.That if, as a result of my having knowingly or recklessly given a false statement at points 1, 2 or 3 above, the University suffers loss, I will make good that loss and indemnify Strathmore University for all action, suits, proceedings, claims, demands and costs occasioned by the University in consequence of my false statement.
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


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record