Research Proposal: Real-time Anxiety Prediction in Virtual Reality Therapy

This paper contains the research proposal of Deniz Mevlevioglu that was presented at the MMSys 2022 doctoral symposium. The benefits of real-time anxiety prediction in Virtual Reality are vast, including uses from therapy to entertainment. These anxiety predictions can be made using biosensors by tracking physiological measurements such as heart rate, electrical brain activity and skin conductivity. However, there are multiple challenges when trying to achieve accurate predictions. First of all, defining anxiety in a useful context and getting objective measurements to predict it can be difficult due to different interpretations of the word. Secondly, personal differences can make it difficult to fit everyone into a generalisable model. Lastly, Virtual Reality strives for immersion, and many systems that use objective measures such as on-body sensors to detect anxiety can make it hard for the user to immerse themselves into the virtual world. Our research aims to come up with a system that will address these problems and manage to get accurate and objective predictions of anxiety in real-time while still allowing the users to be immersed in the experience. To this end, we aim to use fast-performing classification models with multi-modal on-body sensor data to maximise comfort and minimise noise and inaccuracies.