Leveraging multimodal sensory information in cybersickness prediction

Abstract

Cybersickness is one of the problems that undermines user experience in virtual reality. While many studies are trying to find ways to alleviate cybersickness, only a few have considered cybersickness through multimodal perspectives. In this paper, we propose a multimodal, attention-based cybersickness prediction model. Our model was trained based on a total of 24,300 seconds of data from 27 participants and yielded the F1-score of 0.82. Our study results highlight the potential to model cybersickness from multimodal sensory information with a high level of performance and suggest that the model should be extended using additional, diverse samples.