Stress Detection from Photoplethysmography in a Virtual Reality Environment
This addresses the need for objective mental state assessment in personalized virtual reality therapy, though it is incremental as it applies existing PPG methods to a new VR context.
The paper tackled the problem of measuring a patient's mental state in virtual reality exposure therapy by using photoplethysmography (PPG) signals for stress detection, achieving 70.6% accuracy in classifying peaceful and stressful states in a case study with 16 subjects.
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.