Fusing Pressure-Sensitive Mat Data with Video through Multi-Modal Registration
Bibliographic record
Abstract
We have collected a multi-modal neonatal patient dataset suitable for development of noncontact continuous monitoring techniques. Data was simultaneously collected from a RGB-D video camera placed above the patient and a pressure sensitive mat (PSM) beneath the patient. This paper explores the use of various transforms to achieve registration between the video image plane and the PSM, with the ultimate goal of fusing PSM and video modalities of our patient dataset. A series of experiments were conducted to evaluate transforms requiring different numbers of registration landmarks. The expected error in determining landmark locations in both video and PSM is characterized, including the impact of camera offset, registration instrument angle, the degree of collinearity of landmarks, the spacing between landmarks and the use of “secondary” landmarks estimated from patient anatomy. A landmark spacing greater than 450 cm2is recommended since it achieves an error of less than 3 cm when aligning points between video and PSM planes. For a top-down camera view, a similarity transform is recommended while for an angled camera view, a projective transform is recommended.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".