Automatic Recommendation of Strategies for Minimizing Discomfort in Virtual Environments
This work addresses cybersickness, a key barrier to VR adoption in gaming, education, and health, but it is incremental as it builds on existing prediction methods with new data and tools.
The authors tackled the problem of cybersickness in virtual reality by developing a system that predicts when users might experience discomfort, using Random Forest classifiers on a dataset from 84 participants and introducing a questionnaire to assess susceptibility.
Virtual reality (VR) is an imminent trend in games, education, entertainment, military, and health applications, as the use of head-mounted displays is becoming accessible to the mass market. Virtual reality provides immersive experiences but still does not offer an entirely perfect situation, mainly due to Cybersickness (CS) issues. In this work, we first present a detailed review about possible causes of CS. Following, we propose a novel CS prediction solution. Our system is able to suggest if the user may be entering in the next moments of the application into an illness situation. We use Random Forest classifiers, based on a dataset we have produced. The CSPQ (Cybersickness Profile Questionnaire) is also proposed, which is used to identify the player's susceptibility to CS and the dataset construction. In addition, we designed two immersive environments for empirical studies where participants are asked to complete the questionnaire and describe (orally) the degree of discomfort during their gaming experience. Our data was achieved through 84 individuals on different days, using VR devices. Our proposal also allows us to identify which are the most frequent attributes (causes) in the observed discomfort situations.