Real-Time Current Volume Estimation System from an Azure Kinect Camera in Pediatric Intensive Care: Technical Development
Bibliographic record
Abstract
Monitoring respiratory parameters is essential in pediatric intensive care units (PICUs), yet bedside tidal volume (Vt) measurement is rarely performed due to the need for invasive airflow sensors. We present a real-time, non-contact respiratory monitoring system using the Azure Kinect DK (Microsoft, Redmond, WA, USA) depth camera, specifically designed for use in the PICU. The system automatically tracks thoracic volume variations to derive a comprehensive set of ventilator equivalent parameters: tidal volume, respiratory rate, minute ventilation, inspiratory/expiratory times, I:E ratio, and peak flows. Results are displayed via an ergonomic web interface for clinical use. This system introduces several innovations: real-time estimation of a complete set of respiratory parameters, a novel infrared-based region-of-interest detection method using YOLO-OBBs, enabling robust operation regardless of lighting conditions, even in total darkness, making it ideal for continuous monitoring of sleeping patients, and a pixel-wise 3D volume computation method that achieves a mean absolute error under 5% on tidal volume. The system was evaluated on both a healthy adult (compared to spirometry) and a critically ill child (compared to ventilator data). To our knowledge, this is the first study to validate such a contactless respiratory monitoring system on a non-intubated child in the PICU. Further clinical validation is ongoing.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".