Impact of motion artifacts on video-based non-intrusive heart rate measurement
Bibliographic record
Abstract
Measuring vital signs such as heart rate using a camera has the potential to enable better health monitoring for subjects at risk and as such enhance their quality of life. Applications could include driver monitoring via in-dash camera, critical function operator monitoring at work, or remote health monitoring via a webcam. For such a system to be feasible however, it needs to be work well in realistic scenarios where the subject does not sit completely still in front of a camera. Motion artifacts, if not taken into account when designing the system, yield inaccurate results and potentially create false alarms. In this paper, we start with a popular algorithm for extracting heart rate from video based on spatial and temporal filtering, quantify how key parameters used in the algorithm affect its performance in situations when the subject is not sitting still, analyze in detail the performance of the filtering approach in videos with motion, identify issues, and propose approaches to overcome those limitations. The paper shows that the use of wider filters and more levels in the Gaussian pyramid lead to a better performance when the subject is moving, but that the motion artifacts dominate the extracted signal.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".