Computational analysis of valence and arousal in virtual reality gaming using lower arm electromyograms

Progress in the affective computing field has led to the creation of affect-aware games that aim to adapt to the emotions experienced by the players. In this paper we focus on affect recognition in virtual reality (VR) gaming, a problem that to the best of our knowledge has not yet been sufficiently explored. We aim to answer two research questions: (i) Is it possible to reliably capture and recognize the affective state of a person based on EMG sensors placed on their lower arms, while they interact with the virtual environment? and (ii) Is EMG signal from one arm sufficient for detecting affect? We conducted a study in which 8 people were playing a set of VR games with two EMG sensors placed on their arms. We analysed the EMG signals and extracted a number of features to infer the affective states of the players. Our experimental results show that the EMG measures from left and right arms provide sufficient information to detect emotions experienced by a player of a VR game. Our results also show that classifying a DWT-dbl signal with Support Vector Machine (SVM) yields F1=0.91 for predicting low/high arousal and F1=0.85 for predicting positive/negative valence when using just the left-arm EMG signal. To the best of our knowledge, this is the first work that uses EMG data from arm movements as a single source of affective information and addresses affect recognition in VR gaming.