Graph-Based Denoising for Respiration and Heart Rate Estimation During Sleep in Thermal Video
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
Quality sleep is a basic human need for well-being, yet sleep deprivation has been a long-term global problem. A common type of sleep deprivation is obstrucive sleep apnea, where people repeatedly stop breathing during sleep with subsequent abnormal vital signs, namely, respiration rate and heart rate. While tremendous effort has been made for vital signs monitoring systems during sleep, existing works still lack portability for bulky and intrusive systems and reliability for consumer-level, nonintrusive systems. To bridge the gap between practicability and accuracy and facilitate Internet of Things for smart healthcare, in this article, we propose a vital signs estimation system during sleep via a thermal camera. The system first captures thermal image sequences of a sleeping subject and then processes the facial regions within the thermal images for vital signs signal extraction. Specifically, leveraging on the inherent graph structure among subregions of the facial area, we propose a graph-based, spatial–temporal signal denoising scheme. Experimental results show that the graph-based denoising scheme in our system effectively reduces the noise level introduced by cameras and subjects, and our proposed system outperforms state-of-the-art nonintrusive vital signs monitoring systems. Since the algorithm components in our system have relatively low time complexity and no model training is required, our system can be deployed efficiently at the edge devices in a smart home setting. The extracted vital signs can then be used for sleep abnormality detection and disease screening.
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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