Real-time stress assessment through PPG sensor for VR biofeedback
Bibliographic record
Abstract
Existing stress measurement methods, including cortisol measurement, blood pressure monitoring, and psychometric testing, are invasive, impractical, or intermittent, limiting both clinical and biofeedback utility. Better stress measurement methods are needed for practical, widespread application. For the project ViRST, where we use a Virtual Reality (VR) environment controlled by a speech dialog system to provide chronic pain relief, we designed a novel stress biofeedback system. Our prototype employs an ear-clip Photoplethysmogram (PPG) sensor, an Arduino microcontroller, and a supervised learning algorithm. To acquire a training dataset, we ran stress induction experiments on 10 adult subjects aged 30-58 to track Heart Rate Variability (HRV) metrics and Discrete Wavelet Transform (DWT) coefficients. We trained an AdaBoost ensemble classifier to 93% 4-fold cross-validation accuracy and 93% precision. We outline future work to better suit a VR environment and facilitate additional modes of interaction by simplifying the human interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".