Using Trajectory Compression Rate to Predict Changes in Cybersickness in Virtual Reality Games

TL;DR

This paper investigates how the compression rate of movement data in VR can be used to predict cybersickness levels, offering a non-intrusive method for real-time detection in virtual reality games.

Contribution

It introduces a novel approach linking movement data compression rate changes to cybersickness levels and demonstrates machine learning for real-time prediction.

Findings

Clear correlation between compression rate changes and cybersickness

Machine learning effectively identifies cybersickness variations

Feasibility shown for VR applications involving movement

Abstract

Identifying cybersickness in virtual reality (VR) applications such as games in a fast, precise, non-intrusive, and non-disruptive way remains challenging. Several factors can cause cybersickness, and their identification will help find its origins and prevent or minimize it. One such factor is virtual movement. Movement, whether physical or virtual, can be represented in different forms. One way to represent and store it is with a temporally annotated point sequence. Because a sequence is memory-consuming, it is often preferable to save it in a compressed form. Compression allows redundant data to be eliminated while still preserving changes in speed and direction. Since changes in direction and velocity in VR can be associated with cybersickness, changes in compression rate can likely indicate changes in cybersickness levels. In this research, we explore whether quantifying changes in virtual movement can be used to estimate variation in cybersickness levels of VR users. We investigate the correlation between changes in the compression rate of movement data in two VR games with changes in players' cybersickness levels captured during gameplay. Our results show (1) a clear correlation between changes in compression rate and cybersickness, and(2) that a machine learning approach can be used to identify these changes. Finally, results from a second experiment show that our approach is feasible for cybersickness inference in games and other VR applications that involve movement.