Neonatal Face Tracking for Non-Contact Continuous Patient Monitoring
Bibliographic record
Abstract
Noncontact video-based patient monitoring promises several advantages over wearable sensors, particularly for patients in the NICU who have fragile skin. However, such approaches often require definition of a region-of-interest (ROI), such as the patient’s forehead. For example, a number of neonatal monitoring studies have estimated heart rate and respiration from video by first manually cropping the face of the patient before performing analyses within that region. Relying on a static ROI can fail due to patient motion or during clinical interventions, thereby demanding additional manual ROI selection over the course of the monitoring period. Widely used face detection algorithms tend to fail in a neonatal context. We therefore propose a semi-automated method where the ROI is automatically and repeatedly reinitialized to ensure robustness of the ROI for continuous monitoring. Factors such as the displacement of the patient and the change in patient poses are addressed using multiple computer vision techniques before selecting a comprehensive method for ROI tracking. Results were obtained from three patients admitted at the NICU using 20-minute videos including periods of rest, motion, and occlusion events. Compared to a static ROI, the proposed method achieves significantly improved tracking of the patient’s face, as demonstrated by an area under the curve > 0.63 across all patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".