  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.
