Eyes on me: Investigating the role and influence of eye-tracking data on user modeling in virtual reality

Dayoung Jeong, Mingon Jeong, Ungyeon Yang and Kyungsik Han

PLOS ONE, 2022, vol. 17, issue 12, 1-18

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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0278970

DOI: 10.1371/journal.pone.0278970

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