Physiological-Based Emotion Detection and Recognition in a Video Game Context - Sorbonne Université
Conference Papers Year : 2018

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.
Fichier principal
Vignette du fichier
PID5336175.pdf (614.93 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01784795 , version 1 (03-05-2018)

Identifiers

  • HAL Id : hal-01784795 , version 1

Cite

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⟩
443 View
805 Download

Share

More