Wrist pulse measurement and analysis using Eulerian video magnification
Bibliographic record
Abstract
The wrist pulse reveals important information on the health status of a subject. The morphology and characteristics of the wrist pulse play a significant role in analyzing and diagnosing abnormal health conditions. In ancient China, measuring the wrist pulse was considered an important part of the traditional Chinese medicine. Recent studies attempted to quantify this ancient diagnostic technique and proposed several approaches to extract useful information from the wrist pulse signal. In this paper, a digital camera is used to record standard videos of the wrist. The Eulerian video magnification method is employed to detect, non-intrusively, the wrist pulse signals. Then the 2-Gaussian curve modeling method is applied to analyze the signals. Spatial decomposition and temporal filtering are then used on the frames of the recorded videos. The subtle motion and color changes that correlate with the blood flowing through the artery are visualized through amplifying the filtered signal. To verify the performance of the Eulerian video magnification method for detecting the wrist pulse signal, the photoplethysmogram (PPG) pulse signal was measured. The two signals were recorded simultaneously and were compared. The experimental results demonstrate that the Eulerian video magnification method can be used to capture the characteristics of the wrist pulse signal and has the potential for predicting important cardiovascular events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".