Generic Physiological Features as Predictors of Player Experience

Héctor Pérez Martínez, Maurizio Garbarino, Georgios N. Yannakakis

    Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

    Abstrakt

    This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and self- reported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.
    OriginalsprogEngelsk
    TitelACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction
    Antal sider10
    Vol/bind1
    ForlagSpringer Publishing Company
    Publikationsdato2011
    Sider267-276
    ISBN (Trykt)978-3-642-24599-2
    StatusUdgivet - 2011

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