  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.
