Anxiety classification in virtual reality using biosensors: A mini scoping review.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2023
2023
Historique:
received:
21
02
2023
accepted:
15
06
2023
medline:
12
7
2023
pubmed:
10
7
2023
entrez:
10
7
2023
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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.
Identifiants
pubmed: 37428748
doi: 10.1371/journal.pone.0287984
pii: PONE-D-23-04739
pmc: PMC10332625
doi:
Types de publication
Review
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0287984Informations de copyright
Copyright: © 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.
Déclaration de conflit d'intérêts
NO authors have competing interests.
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