Future Blink Prediction in Virtual Reality Using Time-Series Eye Movement Data

Cybersickness induced by virtual reality (VR) ap-plications remains one of the main obstacles to its development. Despite extensive research on reducing cybersickness, there is a lack of non-invasive methods to predict the severity of users' cybersickness in advance. Considering the advancements in eye-tracking technology within VR head-mounted displays and previous studies on the correlation between blinking behavior and cybersickness, this study aims to propose a method for predicting users' future blinking behavior, thereby providing a foundation for subsequent non-invasive cybersickness prediction by leveraging the correlation between blinking and cybersickness. Based on the encoder-decoder architecture, this study compre-hensively considers various ocular movement features of users during a VR experience and develops a blink prediction model using a dataset that records eye movement information from 23 participants during a virtual driving experience. The results show that the model can effectively predict the number of blinks within a future one-second interval. Furthermore, since the number of blinks at the same level of cybersickness can vary rather than remain fixed, the model also predicts the range of blink counts over the next 10 seconds, which can provide a basis for future work on predicting cybersickness.