Dynamic Difficulty Adaptation Based on Stress Detection for a Virtual Reality Video Game: A Pilot Study

Abstract

Virtual reality (VR) is continuing to grow as more affordable technological devices become available. Video games are one of the most profitable applications, while rehabilitation has the most significant social impact. Both applications require a proper user evaluation to provide personalized experiences that avoid boring or stressful situations. Despite the successful applications, there are several opportunities to improve the field of human–machine interactions, one of the most popular ones being the use of affect detection to create personalized experiences. In that sense, this study presents the implementation of two dynamic difficulty adaptation strategies. The person’s affective state is estimated through a machine learning classification model, which later serves to adapt the difficulty of the video game online. The results show that it is possible to maintain the user at a given difficulty level, which is analogous to achieving the well-known flow state. Among the two implemented strategies, no statistical differences were found in the workload induced by the users. However, more physical demands and a higher frustration were induced by one of the strategies, validated with the recorded muscular activity. The results obtained contribute to the state of the art of DDA strategies in virtual reality driven by affective data.