Reflective Multi-PD PPG Sensor for Health Monitoring Under Indoor Ambient Light
Bibliographic record
Abstract
This work presents a flexible, reflective PPG sensor optimized for operation under indoor ambient light without the need for dedicated active illumination such as LED. Using a multi-photodetector (PD) around an optical window, we systematically investigate the impact of window size and light intensity on PPG signal strength and quality. A layered skin model and irradiance simulations in Zemax OpticStudio reveal that reflected light is predominantly center-focused, influencing effective signal capture. AC/DC ratios and AC photocurrents were measured under filtered red and green light at varying lux levels. Results show an optimal performance is achieved at a$10 \times 10 \text{mm}^{2}$window size, beyond which light capture diminishes due to spatial distribution limits. The red filtered light demonstrated superior signal strength at lower lux due to deeper tissue penetration and lower absorption. With the optimized$10 \times 10 \text{mm}^{2}$window size the sensor can acquire reliable PPG signal with detectable AC photocurrents$>10^{-11} \mathrm{A}$and AC/DC >0.008, highlighting its potential for low power, wearable health monitoring applications.
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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 | 0.000 |
| 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) | 0.001 | 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".