An Integrated Wearable Sweat Sensing Patch for Passive Continuous Analysis of Stress Biomarkers at Rest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Real‐time monitoring of mental stress biomarkers in sweat provides the possibility to evaluate mental status in a precise manner. In general, wearable sweat sensors suffer from inconvenient sweat collection, low levels of diagnostic biomarkers in sweat, sophisticated signal processing, and challenges with data visualization. To overcome these challenges, herein an integrated wearable sweat‐sensing patch for continuous analysis of stress biomarkers (cortisol, Mg 2+ , and pH) at rest is demonstrated. The sweat sensing patch comprised a microfluidic chip, a highly sensitive sensing platform, an on‐site signal processing circuitry (SPCs), and a smartphone installed with a home‐developed display software. The sweat collection at rest is realized using a microfluidic chip without perspiration assistance. A ternary composite electrode is designed to obtain good conductivity, high surface area, and massive reactive sites, thereby yielding excellent electrochemical performances and high sensitivity to trace stress biomarkers. The on‐site SPC has the function of signal transduction, conditioning, processing, and wireless transmission. The detection results can be displayed on a smartphone through the software. This work represents a significant scientific and technological advancement toward indexing mental stress status and can be used as an innovative tool for psychological diagnosis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.000 |
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 it