Laser speckle spatiotemporal variance analysis for noninvasive widefield measurements of blood pulsation and pulse rate on a camera‐phone
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it