Assessing Confidence in Video Magnification Heart Rate Measurement using Multiple ROIs
Bibliographic record
Abstract
Heart Rate (HR) is an essential vital sign for assessing the health status of individuals and is clinically measured using electrocardiography (ECG) or Pulse Oximetry. However, there is a need for remote and contactless methods to measure HR where direct contact is not possible. Video Magnification (VM) is a non-contact method to measure HR by magnifying the subtle and minuscule changes in skin tone caused by blood flow. VM performance depends on several factors, including body motion, skin tone, and illumination. For a given region of interest (ROI), HR is identified as the frequency with the largest peak in the spectrum of skin tone modulations; however, spectra may vary across different ROIs and each ROI spectrum may have multiple peaks. In this paper, a confidence metric for VM-based HR is defined as the ratio of the first and second largest spectral peaks. This confidence metric is used to evaluate two different methods for combining disparate ROIs on the face: (1) frequency-domain averaging (FDA) and (2) time-domain averaging (TDA). These methods were tested on 19 subjects with the FDA method showing the correct HR as the largest spectral peak for 16 subjects, and the TDA method showing it correctly for 18 subjects. The average confidence metric was higher for the TDA method (2.9) than the FDA method (2.3) or a single forehead ROI case (2.3), and the confidence metric was generally lowest <tex>$(< 2)$</tex> for subjects with more body motion or darker skin tones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".