Cardiac Monitoring with Textile Capacitive Electrodes in Driving Applications: Characterization of Signal Quality and RR Duration Accuracy
Bibliographic record
Abstract
Capacitive ECG sensors in automobiles enable unobtrusive heart rate monitoring as an indicator of a driver's alertness and health. This paper introduces a capacitive sensor with textile electrodes and provides insights into signal quality and RR duration accuracy. Electrodes of various shapes, sizes, and fabrics were integrated at various positions into the seat back of a driving simulator car seat. Seven subjects completed identical driving circuits with their cardiac signals being recorded simultaneously with textile electrodes and reference Ag-AgCl electrodes. Capacitive ECG signals with observable R peaks (after filtering) could be captured with almost all pairs of textile electrodes, independently of design or placement. Signal quality from textile electrodes was consistently lower compared with reference Ag-AgCl electrodes. Proximity to the heart or even contact with the body seems to be key but not enough to improve signal quality. However, accurate measurement of RR durations was mostly independent of signal quality since 90% of all RR durations measured on capacitive ECG signals had a percentage error below 5% compared to reference ECG signals. Accuracy was actually algorithm-dependent, where a classic Pan-Tompkins-based algorithm was interestingly outperformed by an in-house frequency-domain algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".