# Stress Detection from Photoplethysmography in a Virtual Reality Environment

arXiv: https://arxiv.org/abs/2409.17427 · HTML: https://mlsanity.com/paper/2409.17427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [A Transformer Architecture for Stress Detection from ECG](https://mlsanity.com/paper/2108.09737/) (2021)
- [Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion](https://mlsanity.com/paper/2107.04566/) (2021)

## Abstract (arXiv)

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.