@inproceedings{f5f845ef642b44a8b2404ccc8012e251,
title = "Virtual Reality Data for Predicting Mental Health Conditions",
abstract = "Mental health conditions pose a significant challenge worldwide. Recently, virtual reality (VR) has gained tremendous popularity and has emerged as a promising technological alternative for addressing a variety of mental health conditions. Virtual reality simulates real-world experiences and generates a large amount of virtual data from participants interacting within the virtual world. Harnessing this VR data is key to better understanding participant behaviour and potentially revealing behaviour correlates of mental health conditions. The discovery of such correlates can enable researchers to design and develop targeted VR scenarios or applications for predicting, monitoring, or improving the therapeutic outcomes for mental health conditions. As a result, in this paper, we propose a novel method of using virtual reality data for predicting the presence of mental health conditions.",
keywords = "Data-Machine Learning, Mental Health, Modelling, Prediction, Virtual Reality",
author = "Vibhav Chitale and Daniel Playne and Liang, {Hai Ning} and Nilufar Baghaei",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 21st IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022 ; Conference date: 17-10-2022 Through 21-10-2022",
year = "2022",
doi = "10.1109/ISMAR-Adjunct57072.2022.00011",
language = "English",
series = "Proceedings - 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6--8",
booktitle = "Proceedings - 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022",
}
