Electronic textiles for electrocardiogram monitoring: A review on the structure–property and performance evaluation from fiber to fabric
Bibliographic record
Abstract
The development of electronic textiles used for wearable devices and systems for healthcare monitoring applications has experienced rapid growth in the last decade. Knowledge and understanding of the textile structural hierarchy, as well as the ability to define properties from the fiber and yarn to the fabric level are crucial to the selection of materials and design and performance of wearable systems. However, few studies have approached the selection of optimal e-textile structures with respect to material, electrical, and signal performance properties of sensors used for long-term biological signal monitoring. In this work, a review of e-textile structural properties (fiber, yarn, and fabric) for electrocardiogram (ECG) electrodes is presented, along with their relationship to performance properties including electrical, material, ECG signal quality, fabric hand (sensory perception and quality), and physiological comfort. Considerations and insights into the textile fiber and yarn morphology, electrode structure, design, and construction are outlined. In addition, relevant and upcoming standards for e-textile testing and performance evaluation are summarized. This work serves to organize requirements for ECG textile electrodes into a general reference framework from a bottom-up approach, which can better guide the material selection and design of ECG textile electrodes for wearable 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".