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 Sørensen-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%.I would first like to thank my supervisor Prof. James Green for his support, guidance, and encouragement throughout my graduate studies.His expertise has been invaluable in helping me develop my research skills and complete
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".