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Biometrics and classifier fusion to predict the fun-factor in video gaming

Authors :
Cindy Chamberland
Jean-Christophe Gagnon
Tiago H. Falk
Philip L. Jackson
Mark Parent
Andrea Clerico
Sébastien Tremblay
Pierre-Emmanuel Michon
Source :
CIG
Publication Year :
2016
Publisher :
IEEE, 2016.

Abstract

The key to the development of adaptive gameplay is the capability to monitor and predict in real time the players experience (or, herein, fun factor). To achieve this goal, we rely on biometrics and machine learning algorithms to capture a physiological signature that reflects the player's affective state during the game. In this paper, we report research and development effort into the real time monitoring of the player's level of fun during a commercially available video game session using physiological signals. The use of a triple-classifier system allows the transformation of players' physiological responses and their fluctuation into a single yet multifaceted measure of fun, using a non-linear gameplay. Our results suggest that cardiac and respiratory activities provide the best predictive power. Moreover, the level of performance reached when classifying the level of fun (70% accuracy) shows that the use of machine learning approaches with physiological measures can contribute to predicting players experience in an objective manner.

Details

Database :
OpenAIRE
Journal :
2016 IEEE Conference on Computational Intelligence and Games (CIG)
Accession number :
edsair.doi...........8bb040fabd7f7ad16eef19d825a83389
Full Text :
https://doi.org/10.1109/cig.2016.7860418