Influential Factors in rPPG: Insights from a Diverse and Inclusive Empirical Study
Bibliographic record
Abstract
Remote photoplethysmography (rPPG) allows to optically measure vital signs, such as heart rate, without physical contact. Signal quality impacts the reliability of derived measurements, but the effects of influential factors in rPPG are not well understood. This research specifically examines five factors that are hypothesized to be important: camera type, skin tone, age, gender, and body mass index (BMI). We investigated these factors using a purposely collected dataset from a comparatively large and diverse population (n=126). For each participant, two simultaneous video streams were recorded using different quality hardware to allow to study the effect of choice of camera type. Statistical analysis based on two quality metrics (signal-to-noise ratio and mean absolute error in heart rate measurements) shows that the choice of camera type is important. Generalized linear mixed models provide evidence of a compounded effect between low quality camera and young age with respect to both signal quality decrease and an increase in measurement error. Analysis of the models coefficients brings evidences that darker skin tone also appears to reduce signal quality, but the results are statistically inconclusive in what concerns heart rate measurements. We observe no significant effect of gender or BMI on rPPG in this study. We believe that comprehensive understanding of the influential factors in rPPG will lead to more reliable and inclusive technologies.
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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 | 0.024 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".