Department of Computer Science, University College Cork, Cork, Ireland.
Faculty of Mathematics and Informatics, Transylvania University of Brasov, Brașov Romania.
PLoS One. 2023 Jul 10;18(7):e0287984. doi: 10.1371/journal.pone.0287984. eCollection 2023.
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.
焦虑预测可用于增强虚拟现实应用。我们旨在评估焦虑是否可以在虚拟现实中准确分类的证据。
我们使用 Scopus、Web of Science、IEEE Xplore 和 ACM Digital Library 作为数据源进行了范围审查。我们的搜索包括 2010 年至 2022 年的研究。我们的纳入标准是在虚拟现实环境中进行、使用机器学习分类模型和生物传感器评估用户焦虑的同行评审研究。
确定了 1749 条记录，其中 11 项（n=237）研究被选中。研究的输出数量各不相同，从两个输出到十一个输出。两输出模型的焦虑分类准确性范围为 75%至 96.4%；三输出模型的准确性范围为 67.5%至 96.3%；四输出模型的准确性范围为 38.8%至 86.3%。最常用的测量方法是皮肤电活动和心率。
结果表明，实时确定焦虑状态可以创建高精度模型。但是，应该注意的是，在定义焦虑的真实值方面缺乏标准化，这使得这些结果难以解释。此外，这些研究中的许多研究都包含了主要是学生的小样本，这可能会使结果产生偏差。未来的研究应该非常小心地定义焦虑，并旨在获得更具包容性和更大的样本。研究分类的应用也很重要，通过进行纵向研究来实现。
