2021/08/21 by Diego Monteiro, Monteiro, Diego, Hai‐Ning Liang +5 · 2 citations
Computer Science · #Data Visualization and Analytics #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Image and Video Quality Assessment #Virtual Reality Applications and Impacts
paper · pdf · doi:10.48550/arxiv.2108.09538
openalex publication_date 2021/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying cybersickness in virtual reality (VR) applications such as games\nin a fast, precise, non-intrusive, and non-disruptive way remains challenging.\nSeveral factors can cause cybersickness, and their identification will help\nfind its origins and prevent or minimize it. One such factor is virtual\nmovement. Movement, whether physical or virtual, can be represented in\ndifferent forms. One way to represent and store it is with a temporally\nannotated point sequence. Because a sequence is memory-consuming, it is often\npreferable to save it in a compressed form. Compression allows redundant data\nto be eliminated while still preserving changes in speed and direction. Since\nchanges in direction and velocity in VR can be associated with cybersickness,\nchanges in compression rate can likely indicate changes in cybersickness\nlevels. In this research, we explore whether quantifying changes in virtual\nmovement can be used to estimate variation in cybersickness levels of VR users.\nWe investigate the correlation between changes in the compression rate of\nmovement data in two VR games with changes in players' cybersickness levels\ncaptured during gameplay. Our results show (1) a clear correlation between\nchanges in compression rate and cybersickness, and(2) that a machine learning\napproach can be used to identify these changes. Finally, results from a second\nexperiment show that our approach is feasible for cybersickness inference in\ngames and other VR applications that involve movement.\n