Physiological-Based Emotion Detection and Recognition in a Video Game Context

Abstract : Affective gaming is a hot field of research that exploits human emotion for the enhancement of player's experience during gameplay. Physiological signal is an effective modality that can provide a better understanding of the emotional states and is very promising to be applied to affective gaming. Most physiological-based affective gaming applications evaluate player's emotion on an overall game fragment. These approaches fail to capture the emotion change in the dynamic game context. In order to achieve a better understanding of psychophysiological response with a better time sensitivity, we present a study that evaluates the psychophysiological responses related to the game events. More specifically, we present a multi-modal database DAG that contains peripheral physiological signals (ECG, EDA, respiration, EMG, temperature), accelerometer signals, facial and screening recordings as well as player's self-reported event-related emotion assessment through game playing. We then investigate physiological-based emotion detection and recognition by using machine learning techniques. Common challenges for physiological-based affective model such as signal segmentation, feature normalization, relevant features are addressed. We also discuss factors that influence the performance of the affective models.
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Wenlu Yang, Maria Rifqi, Christophe Marsala, Andrea Pinna. Physiological-Based Emotion Detection and Recognition in a Video Game Context. IEEE International Joint Conference on Neural Networks (IJCNN), Jul 2018, Rio, Brazil. pp.194-201. ⟨hal-01784795⟩

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