A Quantitative Stress Index for Wearable Devices
Bibliographic record
Abstract
Stress detection has been widely studied using physiological signals. However, most research considers stress as a categorical level, limiting the ability to uncover stress’s latent continuous psychological structure. A quantitative representation of stress accommodates inter-individual differences, enhances measurement reliability and sensitivity to changes, and facilitates adaptive real-time applications. This study proposes a framework for estimating a Quantitative Stress Index (QSI), a continuous stress score derived from self-report questionnaires and estimated using physiological features. These features are extracted from Electrodermal Activity (EDA) and Heart Rate Variability (HRV) obtained from Blood Volume Pulse (BVP) signals collected via the Empatica E4 wrist-worn device. The results indicate that the physiological QSI model effectively differentiates between stress and relaxation states, showing improved performance compared to established approaches and demonstrating its potential for quantitative, real-time stress monitoring using wearable sensors.
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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) | 0.006 | 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".