Textile-based Wearable to Monitor Heart Activity in Paediatric Population: A Pilot Study
Bibliographic record
Abstract
Background: Cardiac monitoring for children with heart disease still employs common clinical techniques that require visits to hospital either in an ambulatory or inpatient setting. Frequent cardiac monitoring, such as heart rate monitoring, can limit children's physical activity and quality of life. The main objective of this study is to evaluate the performance of a textile-based device (SKIIN) in measuring heart rate (HR) in different tasks: lying down, sitting, standing, exercising, and cooling down. Methods: Twenty participants including healthy children and children with heart disease were included in this study. The difference between the HRs recorded by the SKIIN was compared with a reference electrocardiogram collection by normalized root mean squared error. Participants completed a questionnaire on their experience wearing the textile device with additional parental feedback on the textile device collected. Results: > 0.05). The normalized root mean squared error was 3.8% ± 3.0% and 3.6% ± 3.7% for healthy and the heart disease groups, respectively. All participants found the textile device non-irritating and easy to wear. Conclusions: This study provides proof of concept that HR can be robustly and conveniently monitored by smart textiles, with similar accuracy to standard-of-care devices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".