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

ABSTRACT:

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%

SUBMITTER: Mevlevioglu D 

PROVIDER: S-EPMC10332625 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature