Real-Time Detection of Simulator Sickness in Virtual Reality Games Based on Players’ Psychophysiological Data during Gameplay
PubDate: Xi’an Jiaotong-Liverpool University
Teams: Jialin Wang; Hai-Ning Liang; Diego Vilela Monteiro; Wenge Xu; Hao Chen; Qiwen Chen
Writers: Jialin Wang; Hai-Ning Liang; Diego Vilela Monteiro; Wenge Xu; Hao Chen; Qiwen Chen
Virtual Reality (VR) technology has been proliferating in the last decade, especially in the last few years. However, Simulator Sickness (SS) still represents a significant problem for its wider adoption. Currently, the most common way to detect SS is using the Simulator Sickness Questionnaire (SSQ). SSQ is a subjective measurement and is inadequate for real-time applications such as VR games. This research aims to investigate how to use machine learning techniques to detect SS based on in-game characters’ and users’ physiological data during gameplay in VR games. To achieve this, we designed an experiment to collect such data with three types of games. We trained a Long Short-Term Memory neural network with the dataset eye-tracking and character movement data to detect SS in real-time. Our results indicate that, in VR games, our model is an accurate and efficient way to detect SS in real-time.