Eulerian Magnification of Multi-Modal RGB-D Video for Heart Rate Estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Eulerian Video Magnification (EVM) has been shown to be highly effective for non-contact, unobtrusive, and non-invasive patient heart rate (HR) estimation systems. EVM is typically applied to RGB video to amplify minute changes in skin color due to varying blood flow, thereby estimating HR. Previous methods require knowledge of the expected HR to optimize the passband to be amplified via EVM. Furthermore, most EVM methods operating on natural light video often fail in low-light environments. This paper proposes a multi-modal selective passband search approach, utilizing predefined EVM passbands, and the use of intelligent data fusion of the three different modalities provided by the Intel RealSense RGB-D camera. We demonstrate the effectiveness of using the color, depth, and near-infrared streams to obtain a consensus HR estimate under various lighting conditions and subject poses. Results indicate that the fusion of HR estimates acquired from each modality is effective and robust to environmental conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it