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Anxiety classification in virtual reality using biosensors: A mini scoping review.
Deniz Mevlevioğlu1, Sabin Tabirca1,2, David Murphy1
1Department of Computer Science, University College Cork, Cork, Ireland.
Plos One
|July 10, 2023
Summary
Anxiety prediction in Virtual Reality (VR) is possible with high accuracy using biosensors and machine learning. However, a lack of standardized anxiety definitions and small sample sizes limit current findings.
Area of Science:
- Human-Computer Interaction
- Computational Neuroscience
- Psychophysiology
Background:
- Anxiety prediction models can enhance Virtual Reality (VR) applications.
- Assessing the evidence for accurate anxiety classification within VR environments is crucial.
Purpose of the Study:
- To conduct a scoping review of studies investigating anxiety classification in Virtual Reality.
- To evaluate the accuracy and methodologies of machine learning models used for anxiety detection in VR.
Main Methods:
- Scoping review of peer-reviewed studies from 2010-2022.
- Searched Scopus, Web of Science, IEEE Xplore, and ACM Digital Library.
- Included studies using machine learning and biosensors for anxiety assessment in VR.
Main Results:
- 11 studies (n=237) were selected from 1749 records.
- Accuracy for two-output models ranged from 75% to 96.4%.
- Electrodermal activity and heart rate were common biosensors.
Conclusions:
- High-accuracy real-time anxiety detection in VR is feasible.
- Lack of standardized anxiety ground truth and small, student-biased samples hinder interpretation.
- Future research requires clear anxiety definitions, larger/diverse samples, and longitudinal studies.
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