Stress Detection from Photoplethysmography in a Virtual Reality Environment
<|reference_start|>Stress Detection from Photoplethysmography in a Virtual Reality Environment: 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.<|reference_end|>
@article{mahmoudi-nejad2024stress,
    title={Stress Detection from Photoplethysmography in a Virtual Reality
  Environment},
    author={Athar Mahmoudi-Nejad, Pierre Boulanger, Matthew Guzdial},
    journal={arXiv preprint arXiv:2409.17427},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17427},
    primaryClass={cs.LG cs.HC}
}