Predicting post-VR game experiences with wearable physiological sensors

Players’ post-game experiences determine their loyalty to a virtual reality (VR) game. However, methods for identifying players’ post-game experiences in the early stages have received far less attention than those for in-game experiences. In this study, we explored the potential of using measurements from wearable physiological sensors to predict players’ post–VR game experiences. The methods employed were correlation analyses and machine learning techniques. The results showed that electrodermal activity (EDA) measurements, particularly the mean EDA and mean EDA peak, are associated with players’ post-VR game experiences after accounting for noise. By utilizing machine learning technology, physiological metrics can forecast players’ diverse reactions after playing VR games with high accuracy. The symptoms of depersonalization/derealization experienced after VR gaming are attributed to being induced by actions within the virtual environment. This research makes significant contributions to the field of user experience recognition and the progression of VR gaming by demonstrating the potential for future VR game centers to analyze player emotions remotely and cost-effectively. This achievement provides the prerequisite for these centers to create tailored new 3D game scenarios to enhance players’ post-game experiences with the support of future advanced generative artificial intelligence technologies.