Stress Detection from Photoplethysmography in a Virtual Reality Environment

TL;DR

This paper presents a VR-based stress detection system utilizing PPG signals to classify mental states, achieving over 70% accuracy, which could improve personalized virtual therapy.

Contribution

It introduces a novel non-intrusive method for assessing mental states in VR using PPG signals, outperforming more complex approaches.

Findings

70.6% classification accuracy in stress detection

PPG signals effectively distinguish between peaceful and stressful states

Simple models outperform complex approaches in this context

Abstract

Personalized virtual reality exposure therapy is a therapeutic practice that can adapt to an individual patient, leading to better health outcomes. Measuring a patient's mental state to adjust the therapy is a critical but difficult task. Most published studies use subjective methods to estimate a patient's mental state, which can be inaccurate. This article proposes a virtual reality exposure therapy (VRET) platform capable of assessing a patient's mental state using non-intrusive and widely available physiological signals such as photoplethysmography (PPG). In a case study, we evaluate how PPG signals can be used to detect two binary classifications: peaceful and stressful states. Sixteen healthy subjects were exposed to the two VR environments (relaxed and stressful). Using LOSO cross-validation, our best classification model could predict the two states with a 70.6% accuracy which outperforms many more complex approaches.