Extracting human breathing rate from the fusion of multiple piezo-resistive membranes
Bibliographic record
Abstract
E-textile sensors present a real potential since they can be integrated into unobtrusive systems such as garments. They allow the monitoring of physiological parameters without disturbing the operator. Although they are mainly used in the medical field for personal health care, these sensors are increasingly used in our daily activities. In sports, at work or at home, embedded sensors are used for on-line human monitoring and for improving the quality of life. The work presented here concerns the processing and evaluation of new sensors for monitoring respiratory activity. Six piezo-resistive membranes are integrated into the backrest of a seat similar to the one you would expect on a commercial aircraft, allowing for non-obtrusive monitoring. The objective is to extract valid and reliable features from the fusion of these sensors in order to provide a non-obtrusive alternative to chest-straps for monitoring respiratory activity. Results show the developed fusion algorithm performs better than a worn reference (chest strap) both in nominal and agitated contexts for monitoring breathing rate.
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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 | 0.001 |
| 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 | 0.000 |
| 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".