Video-Based Breathing Rate Monitoring in Sleeping Subjects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper addresses the challenge of detecting the breathing cessation in sleeping subjects, via breathing pattern monitoring at a distance and under "night-light" conditions. We investigate a near-infrared video-based approach to estimate the breathing rate, based on chest or back movements. A body pose estimation algorithm and the Lucas-Kanade optical flow method are combined to automatically detect the Region of Interest (ROI) represented by a grid of points. The movement of the ROI is then translated into the frequency of respiratory events. We used a dataset with 28 near-infrared videos, as well as 11 videos of subject uncovered and partially covered by blankets. We compared the breathing rate measurements provided by a wearable device with the ones estimated by the video-based approach. A linear correlation analysis of both measurements resulted in a coefficient of determination of 0.925, and accuracy of 99.70% for the first dataset, and 0.873 and 88.95% for the second dataset, respectively. The ultimate application is to detect abnormalities in breathing and health emergencies in environments such as homeless shelters.
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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 | 0.001 |
| 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 it