Using the gameplay and user data to predict and identify causes of cybersickness manifestation in virtual reality games

Virtual reality (VR) is an imminent trend in games, education, entertainment, military, and health applications, as the use of head-mounted displays is accessible to everyone. While VR provides immersive experiences, it still does not offer an entirely perfect situation, mainly due to cybersickness (CS) issues. In this work, we propose a novel approach for predicting upcoming CS symptoms. Our solution is able to suggest whether the user of VR is entering into an illness situation. We adopted random forest classifiers and validated our solution using 16 different machine-learning techniques, which presented the best results. For training purposes, we built our own dataset through a CS profile questionnaire that we also propose in the present work. The questionnaire is focused on registering and identifying the user's susceptibility to CS, considering their historical conditions and also their response to the immersive environment developed by us. In this method, 86 individuals are selected and the developed questionnaire was put to them on different days, and the answers are compiled as dataset. Our proposal also identifying attributes responsible (causes and individual's parameters) for the observed stressful and uncomfortable situations.