Breathing signal combining for respiration rate estimation in smart beds
Why this work is in the frame
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Bibliographic record
Abstract
One of the non-invasive ways to measure respiratory effort is in-bed pressure sensor arrays. Based on the area of the bed and the sensor array covered by a patient's body, some sensors may not include significant respiratory effort components or may have low signal to noise ratios. When combining signals from the different sensors, this can produce a low quality output signal. Signal combiners can overcome this problem. This paper describes two different methods of signal combining to achieve a good estimation of the respiratory rate and the respiratory signal itself. To assess the performance, a participant was asked to lay on the bed in supine position while having normal breathing. Our results indicate that both methods can perform very satisfactorily when compared to a gold standard signal, and that they can outperform some previously published methods.
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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 | 0.001 |
| 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 it