Using Eulerian video magnification framework to measure pulse transit time
Bibliographic record
Abstract
Recent advances in sensor technology and mobile computing are now enabling practical non-intrusive approaches to measure vital signs and other biological signals. Furthermore, most smart phones are now equipped with high resolution cameras and powerful processors that can reliably measure these signals. One of the signals of interest is the pulse transit time that is often correlated with changes in the blood pressure and stress level. Conventional techniques for measuring pulse transit time are based on measuring the electrocardiogram (ECG) signal using leads attached to the chest and measuring the plethysmograph (PPG) signal from a finger. This paper proposes a novel approach to measure pulse transit time non-intrusively using the Eulerian video magnification framework, particularly Eulerian color magnification. The proposed approach uses a video camera to capture a standard video sequence of the subject. After applying spatial decomposition and temporal filtering to the frames, the filtered signal is then amplified to reveal the subtle changing, like the color changing on different spots caused by the blood pulse. Two spots, the wrist and the neck, were selected to measure the pulse transit time. To verify the performance and practicability of the proposed system, the measured pulse transit time were compared with the time difference detected using a conventional technique based on two PulseSensors and the Arduino board. Ten subjects were studied under three status, climbing stairs, five minutes rest after climbing stairs, and twenty minutes rest after climbing stairs. The experimental results show that the pulse transit time measured by the Eulerian video magnification framework is highly correlated with the pulse transit time detected by pulse sensors, demonstrating that the proposed approach has the potential to be used for health-care monitoring.
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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) | 0.001 | 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".