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. © 2011 Springer-Verlag.
CITATION STYLE
Perez Martínez, H., Garbarino, M., & Yannakakis, G. N. (2011). Generic physiological features as predictors of player experience. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6974 LNCS, pp. 267–276). https://doi.org/10.1007/978-3-642-24600-5_30
Mendeley helps you to discover research relevant for your work.