Heart Rate Variability Extraction From Videos Signals: ICA vs. EVM Comparison
Bibliographic record
Abstract
Medical researchers have always been interested in heart rate (HR) and heart rate variability (HRV) analysis. However, nowadays, investigators from a variety of other fields are also probing the subject. Recent advancements in non-contact HR and HRV measurement techniques will likely further boost interest in emotional estimation through HRV. Such measurement methods involve the extraction of the photoplethysmography (PPG) signal from the human's face through a camera. The latest approaches apply independent component analysis (ICA) on the color channels of video recordings to extract a PPG signal. Other investigated methods rely on Eulerian video magnification (EVM) to detect subtle changes in skin color associated with the PPG. To the best of our knowledge, EVM has not been successfully employed to extract HRV features from a video of a human face. In this paper, we present a comparison between our two approaches, one which is based on the ICA and the other is based on EVM. Final results show that the proposed ICA-based method yields better results when it comes to the high frequency (HF) and low frequency over high-frequency (LF/HF) HRV parameters [mean absolute error (MAE) of 0.57 and 0.419] when compared with the EVM-based method (MAE 0.76 and 1.69); however, the second method showed better MAE results for low frequency (LF) and higher correlation with the ground truth. Also our proposed ICA method showed better results in general by improving HF estimates, but the EVM-based method might be more appropriate when motion is involved or when the HF component is not important.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 0.001 |
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".