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dc.creatorOrero, Joseph Onderi
dc.date03/02/2015
dc.dateMon, 2 Mar 2015
dc.dateMon, 2 Mar 2015 14:44:17
dc.dateMon, 2 Mar 2015 14:44:17
dc.date.accessioned2015-03-18T11:29:16Z
dc.date.available2015-03-18T11:29:16Z
dc.identifier
dc.identifier.urihttp://hdl.handle.net/11071/3846
dc.descriptionPaper presented at the International Conference on Physiological Computing Systems (PhyCS), Lisbon, Portugal.
dc.descriptionPhysiological measures have a key advantage as they can provide an insight into human feelings that the subjects may not even be consciously aware of. However, modeling user affective states through pysiology still remains with critical questions especially on the relevant physiological measures for real-life emotionally intelligent applications. In this study, we propose the use of typicality degrees defined according to cognitive science and psychology principles to measure the relevance of the physiological features in characterizing user affective states. Thanks to the typicality degrees, we found consistent physiological characteristics for modeling user affective states.
dc.description.abstractPhysiological measures have a key advantage as they can provide an insight into human feelings that the subjects may not even be consciously aware of. However, modeling user affective states through pysiology still remains with critical questions especially on the relevant physiological measures for real-life emotionally intelligent applications. In this study, we propose the use of typicality degrees defined according to cognitive science and psychology principles to measure the relevance of the physiological features in characterizing user affective states. Thanks to the typicality degrees, we found consistent physiological characteristics for modeling user affective states.
dc.languageeng
dc.publisherSCITEPRESS Digital Library
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dc.subjectAffective Computing
dc.subjectPhysiological Signals
dc.subjectMachine Learning
dc.subjectPrototypes
dc.subjectTypicality Degrees
dc.subjectGameplay
dc.titleTypicality degrees to measure relevance of the physiological signals
dc.typeConference Paper


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