Maintaining Optimal Challenge in Computer Games through Real-Time Physiological Feedback

Abstract

Computer based games are steadily becoming a powerful tool for entertainment and education. When the aim of the game is to keep the player maximally involved, it would be useful to continuously alter the game difficulty level in order to maintain a high level of challenge. This paper presents a framework for providing real-time feedback regarding the affective state (anxiety level) of the player to modify game difficulty. Peripheral physiological signals were measured through wearable biofeedback sensors and an anxiety-based difficulty altering methodology for a Pong game was designed and implemented. The results from affect-elicitation tasks for human participants showed that it is possible to detect affective states of anxiety, engagement, boredom and frustration through physiological sensing in real-time. Performance feedback-based and anxiety-feedback based Pong experiments were then conducted to demonstrate that the latter resulted in higher performance improvement and greater perceived challenge by the participants under lower anxiety.

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