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

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