Abstract
Research has shown that sensor data generated by a user during a VR experience is closely related to the user's behavior or state, meaning that the VR user can be quantitatively understood and modeled. Eye-tracking as a sensor signal has been studied in prior research, but its usefulness in a VR context has been less examined, and most extant studies have dealt with eye-tracking within a single environment. Our goal is to expand the understanding of the relationship between eye-tracking data and user modeling in VR. In this paper, we examined the role and influence of eye-tracking data in predicting a level of cybersickness and types of locomotion. We developed and applied the same structure of a deep learning model to the multi-sensory data collected from two different studies (cybersickness and locomotion) with a total of 50 participants. The experiment results highlight not only a high applicability of our model to sensor data in a VR context, but also a significant relevance of eye-tracking data as a potential supplement to improving the model's performance and the importance of eye-tracking data in learning processes overall. We conclude by discussing the relevance of these results to potential future studies on this topic.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.