Eyes on me: Investigating the role and influence of eye-tracking data on user modeling in virtual reality
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Dayoung | - |
dc.contributor.author | Jeong, Mingon | - |
dc.contributor.author | Yang, Ungyeon | - |
dc.contributor.author | Han, Kyungsik | - |
dc.date.accessioned | 2023-03-13T04:29:10Z | - |
dc.date.available | 2023-03-13T04:29:10Z | - |
dc.date.created | 2023-03-08 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/182436 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PUBLIC LIBRARY SCIENCE | - |
dc.title | Eyes on me: Investigating the role and influence of eye-tracking data on user modeling in virtual reality | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Han, Kyungsik | - |
dc.identifier.doi | 10.1371/journal.pone.0278970 | - |
dc.identifier.scopusid | 2-s2.0-85145242103 | - |
dc.identifier.wosid | 000925807400018 | - |
dc.identifier.bibliographicCitation | PLOS ONE, v.17, no.12, pp.1 - 18 | - |
dc.relation.isPartOf | PLOS ONE | - |
dc.citation.title | PLOS ONE | - |
dc.citation.volume | 17 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 18 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | adult | - |
dc.subject.keywordPlus | Article | - |
dc.subject.keywordPlus | attention | - |
dc.subject.keywordPlus | behavior | - |
dc.subject.keywordPlus | controlled study | - |
dc.subject.keywordPlus | cybersickness | - |
dc.subject.keywordPlus | deep learning | - |
dc.subject.keywordPlus | eye tracking | - |
dc.subject.keywordPlus | female | - |
dc.subject.keywordPlus | human | - |
dc.subject.keywordPlus | information processing | - |
dc.subject.keywordPlus | learning | - |
dc.subject.keywordPlus | locomotion | - |
dc.subject.keywordPlus | male | - |
dc.subject.keywordPlus | prediction | - |
dc.subject.keywordPlus | virtual reality | - |
dc.identifier.url | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278970 | - |
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