Towards an Intelligent Bed Sensor: Non-intrusive Monitoring of Sleep Irregularities with Computer Vision Techniques
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel approach for monitoring sleep using pressure data. The goal of sleep monitoring is to detect and log events of normal breathing, sleep apnea and body motion. The proposed approach is based on translating the signal data to the image domain by computing a sequence of inter-frame similarity matrices from pressure maps acquired with a mattress of pressure sensors. Periodicity analysis was performed on similarity matrices via a new algorithm based on segmentation of elementary patterns using the watershed transform, followed by aggregation of quasi-rectangular patterns into breathing cycles. Once breathing events are detected, all remaining elementary patterns aligned on the main diagonal are considered as belonging to either apnea or motion events. The discrimination between these two events is based on detecting movement times from a statistical analysis of pressure data. Experimental results confirm the validity of our approach.
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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 | 0.000 |
| 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