Illumination-compensated non-contact imaging photoplethysmography via dual-mode temporally coded illumination
Bibliographic record
Abstract
Non-contact camera-based imaging photoplethysmography (iPPG) is useful for measuring heart rate in conditions where contact devices are problematic due to issues such as mobility, comfort, and sanitation. Existing iPPG methods analyse the light-tissue interaction of either active or passive (ambient) illumination. Many active iPPG methods assume the incident ambient light is negligible to the active illumination, resulting in high power requirements, while many passive iPPG methods assume near-constant ambient conditions. These assumptions can only be achieved in environments with controlled illumination and thus constrain the use of such devices. To increase the number of possible applications of iPPG devices, we propose a dual-mode active iPPG system that is robust to changes in ambient illumination variations. Our system uses a temporally-coded illumination sequence that is synchronized with the camera to measure both active and ambient illumination interaction for determining heart rate. By subtracting the ambient contribution, the remaining illumination data can be attributed to the controlled illuminant. Our device comprises a camera and an LED illuminant controlled by a microcontroller. The microcontroller drives the temporal code via synchronizing the frame captures and illumination time at the hardware level. By simulating changes in ambient light conditions, experimental results show our device is able to assess heart rate accurately in challenging lighting conditions. By varying the temporal code, we demonstrate the trade-off between camera frame rate and ambient light compensation for optimal blood pulse detection.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".