An Integrated Wearable Sweat Sensing Patch for Passive Continuous Analysis of Stress Biomarkers at Rest
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.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".