Anxiety classification in virtual reality using biosensors: A mini scoping review

dc.contributor.authorMevlevioğlu, Denizen
dc.contributor.authorTabirca, Sabinen
dc.contributor.authorMurphy, Daviden
dc.contributor.editorMd. Milon Islamen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-10-13T09:12:39Z
dc.date.available2023-10-13T09:12:39Z
dc.date.issued2023-07-10en
dc.description.abstractBackground: Anxiety prediction can be used for enhancing Virtual Reality applications. We aimed to assess the evidence on whether anxiety can be accurately classified in Virtual Reality. Methods: We conducted a scoping review using Scopus, Web of Science, IEEE Xplore, and ACM Digital Library as data sources. Our search included studies from 2010 to 2022. Our inclusion criteria were peer-reviewed studies which take place in a Virtual Reality environment and assess the user’s anxiety using machine learning classification models and biosensors. Results: 1749 records were identified and out of these, 11 (n = 237) studies were selected. Studies had varying numbers of outputs, from two outputs to eleven. Accuracy of anxiety classification for two-output models ranged from 75% to 96.4%; accuracy for three-output models ranged from 67.5% to 96.3%; accuracy for four-output models ranged from 38.8% to 86.3%. The most commonly used measures were electrodermal activity and heart rate. Conclusion: Results show that it is possible to create high-accuracy models to determine anxiety in real time. However, it should be noted that there is a lack of standardisation when it comes to defining ground truth for anxiety, making these results difficult to interpret. Additionally, many of these studies included small samples consisting of mostly students, which may bias the results. Future studies should be very careful in defining anxiety and aim for a more inclusive and larger sample. It is also important to research the application of the classification by conducting longitudinal studies.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleide0287984en
dc.identifier.citationMevlevioğlu, D., Tabirca, S. and Murphy, D. (2023) ‘Anxiety classification in virtual reality using biosensors: A mini scoping review’, PLOS ONE, 18(7), e0287984 (15 pp). https://doi.org/10.1371/journal.pone.0287984.en
dc.identifier.doi10.1371/journal.pone.0287984en
dc.identifier.endpage15en
dc.identifier.issn1932-6203en
dc.identifier.issued7en
dc.identifier.journaltitlePLOS ONEen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/15118
dc.identifier.volume18en
dc.language.isoenen
dc.publisherPLOSen
dc.relation.ispartofPLOS ONEen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Centres for Research Training Programme::Data and ICT Skills for the Future/18/CRT/6222/IE/SFI Centre for Research Training in Advanced Networks for Sustainable Societies/en
dc.relation.urihttps://doi.org/10.1371/journal.pone.0287984en
dc.rights© 2023 Mevlevioğlu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectVirtual Reality (VR)en
dc.subjectAnxiety disordersen
dc.subjectVR therapyen
dc.subjectTherapyen
dc.subjectAnxietyen
dc.titleAnxiety classification in virtual reality using biosensors: A mini scoping reviewen
dc.typeArticle (peer-reviewed)en
oaire.citation.issue7en
oaire.citation.volume18en
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