Depth-based Patient Monitoring in the NICU with Non-Ideal Camera Placement
Bibliographic record
Abstract
Depth cameras can improve the performance of patient monitoring systems without the introduction of multiple sensors in the NICU.A method was developed to correct non-ideal camera placement.The mean absolute percentage error of the method tested on 28 patients was 5.58 for camera angles up to 38.58 away from the optimal camera placement.An ROI selection method was developed and tested for the use of extracting a respiratory rate signal.The ROI selection method was found to have an average Srensen-Dice coefficient of 0.62 and Jaccard index of 0.46.The signal was compared to a simpler method resulting in an improvement to the percentage of acceptable estimates.An intervention detection method was developed using a vision transformer model, and the performance was compared to the state-of-the-art in the field.The best model was found to achieve a sensitivity of 85.6%, precision of 89.8%, and F1-Score of 87.6%.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".