Template-based respiratory monitoring and tidal volume estimation
Bibliographic record
Abstract
Vision-based monitoring has gained attention as a non-invasive alternative to conventional contact-based methods in intensive care units (ICUs). While prior studies highlight its potential for respiratory monitoring, the approach remains nascent, with challenges such as heuristically defined respiratory-relevant regions. This lack of anatomically consistent definitions can undermine the reliability of volume estimation techniques. Typically, patient-specific calibration data are required to address this limitation, yet such data are frequently unavailable in practical settings. In this study, we introduce a novel method for modeling the torso region to enable precise respiratory monitoring and volume estimation. Using a Kinect camera, we capture the patient's body surface, which is then aligned and registered with a standardized human template for consistent segmentation of the respiration-relevant region. Subsequently, an efficient and accurate volume computation algorithm extracts respiratory parameters and monitors tidal volume. We validated our approach on a cohort of volunteers, benchmarking it against gold-standard spirometry and expert annotations. Overall, the proposed method achieves over 90% accuracy in tidal volume estimation and 95% accuracy in respiratory rate estimation, without relying on patient-specific calibration. These results demonstrate its robustness and suitability for ICU respiratory monitoring.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".