Extracting Heart Rate Variability: A Summary of Camera Based Photoplethysmograph
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
Heart rate is one of the major indicators of our physiological state. An irregular or rapid heartbeat, fainting, dizziness, chest pain or shortness of breath can be found by it. The traditional heart rate observing methods such as electrocardiogram (ECG) require physical contact in order to show the heart rate reading exactly but this is uncomfortable for regular monitoring. Techniques for measuring physiological parameters remotely from hospital, as well as monitoring patients continuously, have been one of the major concerns of the scholars. Many heart rate measurement methods using smartphone, webcam, commercial camera etc. have been proposed by many researchers. Image or video processing is the fundamental technique for measuring heart rate through smartphone. With the aim of exploring different heart rate monitoring methods and the advantages and disadvantages, the present study consulted secondary sources like published articles.
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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 | 0.001 |
| 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