3D‐Knit Dry Electrodes using Conductive Elastomeric Fibers for Long‐Term Continuous Electrophysiological Monitoring
Bibliographic record
Abstract
Abstract Recent advances in telemedicine and personalized healthcare have motivated new developments in wearable technologies targeting continuous monitoring of biosignals. Common limitations of wearables for continuous monitoring include durability and breathability of their biopotential electrodes. This paper tackles this challenge by proposing flexible, breathable, and washable dry textile electrodes made of conductive elastomeric filaments (CEFs). First, candidate CEF fibers are characterized. Using an industrial knitting machine, CEF fibers are then directly knitted into textile electrodes. To assess their performance in more realistic circumstances, smart garments with textile electrodes are knitted. Electrocardiograms (ECGs) are acquired using an underwear garment and electrooculograms (EOGs) are acquired using a headband. ECGs and EOGs with textile electrodes are found to have comparable fidelity to that of the gold standard gel electrodes. CEF electrodes are also resistant to repeated wash and dry cycles (30×) and continue to acquire high‐fidelity biosignals. Smart underwear garments are also used to perform continuous ECG measurements in five participants over 24 h of unrestricted daily activities. Results demonstrate the success of these garments in performing high fidelity continuous ECG monitoring. Collectively, these results present CEF electrodes as a promising scalable solution to the challenges of wearable technologies for long‐term continuous electrophysiological monitoring applications.
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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 | 0.000 | 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".