AwareOne: A Wrist System for Daily Stress Monitoring Using Mid-Level Physiological Fusion and Late-Fusion with Survey-Based Labels
Bibliographic record
Abstract
Background: Multi-sensor fusion can improve daily stress monitoring. Methods: A wrist-worn device includes a system of the Galvanic Skin Response (GSR), PPG-derived Heart Rate Variability (HRV), skin temperature, and SpO₂, paired with self-reported questionnaires. The device streams data to a mobile app over Bluetooth Low Energy and updates the UI within 1-2 seconds. The physiological features are taken within a fixed window around each questionnaire time and performs a mid-level fusion; late fusion is also evaluated with self-reports. Results: Against a commercial reference device, AwareOne achieved a mean absolute error of 0.23 for SpO₂ and 4.94 for BPM in a one-day benchmark session. The system was validated through a technical evaluation using representative inputs and simulated survey labels. A Support Vector Machine algorithm reached a mean squared error of 0.08 on stress prediction. Temperature showed to have the strongest correlation with simulated stress levels at −0.43, followed by Heart Rate Variability (HRV) at 0.36, while SpO₂ had negligible correlation at 0.09 in the current dataset. Conclusion: The system integrates multi-sensing, on device preprocessing, BLE transmission, and a clear fusion workflow that creates a useful predictive performance of daily stress monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".