A Confidence Framework Through Temporal Averaging for Heart Rate Estimation in Video Magnification
Bibliographic record
Abstract
Heart rate (HR) is an essential indicator of cardiovascular disorders as well as other internal physiological complications. Normally, HR tends to vary over time with larger variations associated with external factors like stress or exercise. Video Magnification (VM) can remotely measure HR from video data acquired with an RGB/Thermal camera. Incorporating VM into digital health monitoring systems could be essential in supporting and maintaining well-being and health, especially for an aging population. Previous work has explored factors affecting VM performance such as skin tone, body motion, and light conditions as well as different facial regions of interest (ROI). The current work extends a confidence model for VM methods by proposing temporal combination of partially overlapping consecutive windows. Results from 19 subjects show that temporal averaging enhances VM performance and improves overall HR accuracy from 61.1% to 68.4%. HR errors are associated with motion artifacts and/or darker skin tones. The confidence metric is generally proportional to the accuracy of the HR estimate, providing an indication of when VM-based HR measurements are reliable.
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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".