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Virtual Reality (VR) technology has been proliferating in the last decade,especially in the last few years. However, Simulator Sickness (SS) stillrepresents a significant problem for its wider adoption. Currently, the mostcommon way to detect SS is using the Simulator Sickness Questionnaire (SSQ).SSQ is a subjective measurement and is inadequate for real-time applicationssuch as VR games. This research aims to investigate how to use machine learningtechniques to detect SS based on in-game characters' and users' physiologicaldata during gameplay in VR games. To achieve this, we designed an experiment tocollect such data with three types of games. We trained a Long Short-TermMemory neural network with the dataset eye-tracking and character movement datato detect SS in real-time. Our results indicate that, in VR games, our model isan accurate and efficient way to detect SS in real-time.

Real-Time Detection of Simulator Sickness in Virtual Reality Games Based on Players' Psychophysiological Data during Gameplay