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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.17427 |
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| _version_ | 1866909326978842624 |
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| author | Mahmoudi-Nejad, Athar Boulanger, Pierre Guzdial, Matthew |
| author_facet | Mahmoudi-Nejad, Athar Boulanger, Pierre Guzdial, Matthew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17427 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stress Detection from Photoplethysmography in a Virtual Reality Environment Mahmoudi-Nejad, Athar Boulanger, Pierre Guzdial, Matthew Machine Learning Human-Computer Interaction 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. |
| title | Stress Detection from Photoplethysmography in a Virtual Reality Environment |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.17427 |