가상현실에서의 개인 맞춤형 사이버멀미 선제적 예측 모델 개발

초록

To mitigate cybersickness, which disrupts the usability of virtual reality (VR), recent studies have proposed prediction models using artificial intelligence. However, most of these models can only be applied after users have experienced cybersickness, limiting their usefulness for proactive response. The importance of personalized cybersickness prediction models is underscored by the fact that the Fast Motion Sickness Scale (FMS), an indicator for measuring cybersickness, is somewhat subjective for each individual. This paper proposes a model for the early prediction of cybersickness based on sensor data from a Head Mounted Display (HMD), utilizing a long-term time-series forecasting model called PatchTST. The experimental results demonstrate that the proposed model achieved performance comparable to the baseline models, with a mean absolute error (MAE) of 0.14 and a root mean square error (RMSE) of 0.67. By leveraging PatchTST’s the potential for early prediction, we developed a personalized model that achieved an accuracy of 0.71. These results suggest the feasibility of an early prediction model for cybersickness and highlight the potential for developing personalized models.

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