Motion Sickness Analysis Based On Postural Sway And Eye Movement In Virtual Reality

Motion sickness is a physiological phenomenon that often occurs in people’s daily life,for example,when people are traveling on vehicles,such as cars and boats.With the rapid development of virtual reality technology,various virtual reality devices have appeared in our daily use.However,people often feel dizziness,nausea and other motion-related symptoms when experiencing virtual scenes,which greatly reduces the user experience and hinders the promotion and development of virtual reality technology.Therefore,research on motion sickness has become very urgent.How to judge whether motion sickness occurs or to predict the degree of motion sickness has become an important issue in the field of motion sickness research.Aiming at the above two problems and based on the environment of virtual reality at the same time,this paper starts with two physiological characteristics which are postural sway and eye movement changes caused by motion sickness.For postural sway,we experimentally analyzed the relationship between the speed of visual stimulus,postural sway angle and the degree of motion sickness,and designed a system that can predict the degree of motion sickness in real time in virtual environment.For eye movement,this article introduced visual saliency into the study of motion sickness for the first time,and experimentally established the first eye movement database for people in normal state or motion sickness state in virtual environment.Based on the database,we first analyze the differences and features of human visual attention in two cases,and then establish two deep learning models.One is a visual saliency prediction model that predicts the visual attention area of people with motion sickness.This can help us better understand the attention mechanism of patients with motion sickness,and can be used to guide virtual reality hardware or virtual environment content.The other is a visual saliency classification model,which is used to determine whether the user has motion sickness according to the user’s visual attention data.This motion sickness classification model based on eye movement data solves the problem that previous methods are difficult to practically use,and can allow users to have a better experience in virtual reality and prevent the deepening of motion sickness.Both models are excellent in performance and are in the leading position in the industry.