Stress Inference in a Virtual Reality Game for Rehabilitation with Body Motion and Heart Rate

This paper proposes an architecture for detecting stress in a player, while playing a Virtual Reality (VR) game, by analyzing the player's movements as well as the player's Heart Rate (HR). For this effect, only a camera to analyze players' Body Motion Rate (BMR) and a smartwatch to capture the HR, were used. As part of the computation of the BMR, computer vision techniques were used to detect the player's skeleton, computing the difference between frames. A dataset was captured in this paper, while the players tested 5 different scenarios to induce different stress situations. The proposed dataset serves as a proof of concept to validate the relation between HR and BMR. Future work should investigate synthetic data generation techniques to improve dataset diversity and adaptability for Dynamic Difficulty Adjustment (DDA) systems. This research contributes to advancing stress detection in VR, with potential applications in rehabilitation, particularly for conditions such as schizophrenia, promoting improved well-being and stress management in the long run.