Performance and Signal Quality Analysis of Electrocardiogram Textile Electrodes for Smart Apparel Applications
Bibliographic record
Abstract
Electronic textile (e-textile) systems applied to biological signal monitoring are of great interest to the healthcare industry, given the potential to provide continuous and long-term monitoring of healthy individuals and patients. Most developments in e-textiles have focused on novel materials and systems without systematic considerations into how the hierarchical structure of fibrous assemblies may influence performance and compatibility of the materials during use. This study examines mechanisms underlying the stability and quality of textile-based electrocardiogram (ECG) electrodes used in a smart bra. Signal quality of the biometric data obtained affects feedback and user experience and may be influenced by characteristics and properties of the material. Under stationary and dynamic conditions, analysis of the raw ECG signal and heart rate, with respect to textile-electrode material properties have been performed. Currently, there is no standardized procedure to compare the ECG signal between electrode materials. In this study, several methods have been applied to compare differences between silver-based textile electrodes and silver/silver-chloride gel electrodes. The comparison methods serve to complement visual observations of the ECG signal acquired, as possible quantitative means to differentiate electrode materials and their performance. From the results obtained, signal quality, and heart rate (HR) detection were found to improve with increased skin contact, and textile structures with lower stretch and surface resistance, especially under dynamic/movement test conditions. It was found that the performance of the textile electrode materials compared exceeded ECG signal quality thresholds previously established for acceptable signal quality, specifically for the kurtosis ( K > 5), and Pearson correlation coefficients ( r ≥ 0.66) taken from average ECG waveforms calculated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".