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

TL;DR

This paper presents a machine learning approach using LSTM neural networks to detect simulator sickness in VR games in real-time, based on physiological and in-game data, addressing limitations of subjective questionnaires.

Contribution

It introduces a novel real-time detection method for simulator sickness using physiological data and LSTM models, improving upon subjective assessment tools.

Findings

LSTM model accurately detects SS during gameplay

Physiological data effectively predicts SS in real-time

Method outperforms traditional SSQ assessments

Abstract

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.