Predicting cybersickness based on user's gaze behaviors in HMD-based virtual reality. Issue 2 (28th February 2021)

• Record Type: Journal Article
• Authors: Chang, Eunhee; Kim, Hyun Taek; Yoo, Byounghyun
• Is Part Of: Journal of computational design and engineering. Volume 8:Issue 2(2021)
• Page Start: 728
• Page End: 739
• Publication Date: 2021-02-28
• Subjects: cybersickness -- virtual reality -- eye-tracking, head-mounted display -- subjective vertical mismatch theory -- regression analysis; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwab010
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: Cybersickness refers to a group of uncomfortable symptoms experienced in virtual reality (VR). Among several theories of cybersickness, the subjective vertical mismatch (SVM) theory focuses on an individual's internal model, which is created and updated through past experiences. Although previous studies have attempted to provide experimental evidence for the theory, most approaches are limited to subjective measures or body sway. In this study, we aimed to demonstrate the SVM theory on the basis of the participant's eye movements and investigate whether the subjective level of cybersickness can be predicted using eye-related measures. 26 participants experienced roller coaster VR while wearing a head-mounted display with eye tracking. We designed four experimental conditions by changing the orientation of the VR scene (upright vs. inverted) or the controllability of the participant's body (unrestrained vs. restrained body). The results indicated that participants reported more severe cybersickness when experiencing the upright VR content without controllability. Moreover, distinctive eye movements (e.g. fixation duration and distance between the eye gaze and the object position sequence) were observed according to the experimental conditions. On the basis of these results, we developed a regression model using eye-movement features and found that our model can explain 34.8% of the total variance of cybersickness, indicating a substantial improvement compared to the previous work (4.2%). This study provides empirical data for the SVM theory using both subjective and eye-related measures. In particular, the results suggest that participants' eye movements can serve as a significant index for predicting cybersickness when considering natural gaze behaviors during a VR experience. Graphical Abstract: