Noncontact Vision-Based Cardiopulmonary Monitoring in Different Sleeping Positions
Why this work is in the frame
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Bibliographic record
Abstract
Individuals with obstructive sleep apnea (OSA) can experience partial or complete collapse of the upper airway during sleep. This condition affects between 10-17% of adult men and 3-9% of adult women, requiring arousal to resume regular breathing. Frequent arousals disrupt proper sleeping patterns and cause daytime sleepiness. Untreated OSA has been linked to serious medical issues including cardiovascular disease and diabetes. Unfortunately, diagnosis rates are low (∼20%) and current sleep monitoring options are expensive, time consuming, and uncomfortable. Toward the development of a convenient, noncontact OSA monitoring system, this paper presents a simple, computer vision-based method to monitor cardiopulmonary signals (respiratory and heart rates) during sleep. System testing was performed with 17 healthy participants in five different simulated sleep positions. To monitor cardiopulmonary rates, distinctive points are automatically detected and tracked in infrared image sequences. Blind source separation is applied to extract candidate signals of interest. The optimal respiratory and heart rates are determined using periodicity measures based on spectral analysis. Estimates were validated by comparison to polysomnography recordings. The system achieved a mean percentage error of 3.4% and 5.0% for respiratory rate and heart rate, respectively. This study represents an important step in building an accessible, unobtrusive solution for sleep apnea diagnosis.
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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 | 0.001 | 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 it