Remote physiological monitoring of neck blood vessels with a high-speed camera
Bibliographic record
Abstract
Introduction Several population-based clinical studies suggest that increased Pulse Wave Velocity (PWV) is highly associated with increased cardiovascular disease (CVD) mortality, which is one of the leading causes of death worldwide. Current methods for CVD detection are invasive, expensive, and contact methods, which are not friendly for skin-sensitive patients. Methods In this study, we investigated the use of remote photoplethysmography (rPPG) on the neck region using a high-speed camera (2000 frames per second (fps)) to resolve the drawbacks of CVD detection and overcome the limitations of current PWV measurement techniques. Pearson correlation and cross-correlation were used for signal processing and generating the projection map of potential major vessels. A reference signal is selected for the region of interest based on peak value and modulation depth variation. The signal distance and pulse transit time (PPT) between the local and reference signals were calculated using the cross-correlation method and then fitted into a linear regression model for PWV calculation. Results The results revealed areas on the neck that positively and negatively correlated with the selected reference signals, potentially representing the distribution of the main neck vessels - carotid artery and jugular vein- and, consequently, the upstream and downstream blood circulation directions. Discussion This research implies the feasibility of touchless estimation of local PWV using a high-speed camera, expanding the potential applications of remote photoplethysmography in aiding the diagnosis of CVD.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".