Laser speckle spatiotemporal variance analysis for noninvasive widefield measurements of blood pulsation and pulse rate on a camera‐phone
Bibliographic record
Abstract
Photoplethysmography is a well‐established technique for the noninvasive measurement of blood pulsation. However, photoplethysmographic devices typically need to be in contact with the surface of the tissue and provide data from a single contact point. Extensions of conventional photoplethysmography to measurements over a wide field‐of‐view exist, but require advanced signal processing due to the low signal‐to‐noise‐ratio of the photoplethysmograms. Here, we present a noncontact method based on temporal sampling of time‐integrated speckle using a camera‐phone for noninvasive, widefield measurements of physiological parameters across the human fingertip including blood pulsation and resting heart‐rate frequency. The results show that precise estimation of these parameters with high spatial resolution is enabled by measuring the local temporal variation of speckle patterns of backscattered light from subcutaneous skin, thereby opening up the possibility for accurate high resolution blood pulsation imaging on a camera‐phone. Camera‐phone laser speckle imager along with measured relative blood perfusion maps of a fingertip showing skin perfusion response to a pulse pressure applied to the upper arm. The figure is for illustration only; the imager was stabilized on a stand throughout the experiments. magnified image Camera‐phone laser speckle imager along with measured relative blood perfusion maps of a fingertip showing skin perfusion response to a pulse pressure applied to the upper arm. The figure is for illustration only; the imager was stabilized on a stand throughout the experiments.
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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) | 0.004 | 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".