Machine Learning Assessment of Heart Rate Confidence from Video Magnification
Bibliographic record
Abstract
Video magnification (VM) provides an alternative health monitoring solution by enabling contactless and remote measurement of vital signs such as heart rate (DR).DR is a crucial biomarker for assessing the overall health of individuals, and the increased need to replace traditional wearable devices and avoid complex applications of sensors makes VM essential for in-home health assessment. VM can measure HR by detecting subtle changes of skin color due to blood flow, however, external factors such as subject or camera motion, variable lighting and skin tone can greatly affect performance. Previous work implemented methods that improved the overall accuracy of VM-based HR predictions but did not indicate if a given HR prediction was likely to be correct or incorrect. This work assesses the confidence of the predicted HR using a set of features derived during the VM process. Several machine learning models were evaluated to analyze the confidence parameters from three different VM processing methods. The results show an accuracy of92.1% for an SVM model that classifies if VM-based HR is correct or incorrect. The high classification accuracy indicates the effectiveness of the confidence metrics for discriminating between correct and incorrect HR predictions using VM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".