Development of a photoplethysmography testing platform using stereolithography 3D printing to create human finger optical phantoms with vascular channels for wearable device applications
Bibliographic record
Abstract
Photoplethysmography (PPG) is a widely used, non-invasive optical technique but is affected by various confounding factors in human skin, such as patient pigmentation, sensor geometries, and blood composition. There is a need for adaptable testing platforms to better understand how these confounders impact optical sensors. This study aims to develop a low-cost, reproducible platform for rapid assessment of PPG devices. An anatomical finger model was created using SolidWorks, with U-shaped digital palmar arteries designed and printed in photopolymer resin through stereolithography. Pulsations simulating blood flow were generated using a peristaltic pump and a blood analog solution. Several vessel diameters were tested to simulate human vasculature, with successful reproducibility observed for diameters greater than 2.8mm. Pulsatile signals were captured using multi-wavelength PPG, spanning from blue to infrared light. The system is highly customizable, with interchangeable components that offer flexibility to be adjusted and can ameliorate the burden of testing on patients and animals. Future work will integrate an epidermal layer containing melanin to more accurately model skin interactions. This platform is timely, given the ongoing investigation by regulatory bodies into pulse oximeter testing and its role in equitable device development.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".