Measuring chest movement using an array of unobstusive pressure sensors
Bibliographic record
Abstract
The use of unobtrusive sensors for physiological monitoring is growing in popularity. Advantages such as their non-contact nature and their limited cognitive demand on the user can increase acceptance and usefulness in certain populations. Biomedical applications for unobtrusive pressure sensors include the analysis of bed transfer sequences and the extraction of breathing rate during long-term trend analysis and health monitoring. This paper proposes an algorithm to measure chest wall motion using an unobtrusive pressure sensor array in a combined signal format and compare it to measurements derived from respiratory inductance plethysmography bands. The novel contributions are using an unobtrusive pressure sensor array beneath the mattress to determine the duration of inspiration and expiration segments, and to determine the timing of each movement. The cross-correlation was used to align signals for the pressure array. The durations of the movements were statistically similar, and the timing of the identified movements overlapped considerably (>80%) when the signals were aligned using the calculated delay value.
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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".