Ultrasoft Long-Lasting Reusable Hydrogel-Based Sensor Patch for Biosignal Recording
Bibliographic record
Abstract
Here, we report an ultrasoft extra long-lasting, reusable hydrogel-based sensor that enables high-quality electrophysiological recording with low-motion artifacts. The developed sensor can be used and stored in an ambient environment for months before being reused. The developed sensor is made of a self-adhesive electrical-conductivity-enhanced ultrasoft hydrogel mounted in an Ecoflex-based frame. The hydrogel’s conductivity was enhanced by incorporating polypyrrole (PPy), resulting in a conductivity of 0.25 S m−1. Young’s modulus of the sensor is only 12.9 kPa, and it is stretchable up to 190%. The sensor was successfully used for electrocardiography (ECG) and electromyography (EMG). Our results indicate that using the developed hydrogel-based sensor, the signal-to-noise ratio of recorded electrophysiological signals was improved in comparison to that when medical-grade silver/silver chloride (Ag/AgCl) wet gel electrodes were used (33.55 dB in comparison to 22.16 dB). Due to the ultra-softness, high stretchability, and self-adhesion of the developed sensor, it can conform to the skin and, therefore, shows low susceptibility to motion. In addition, the sensor shows no sign of irritation or allergic reaction, which usually occurs after long-term wearing of medical-grade Ag/AgCl wet gel electrodes on the skin. Further, the sensor is fabricated using a low-cost and scalable fabrication process.
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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 | 0.000 | 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 | 0.001 |
| 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".