Unobtrusive Respiratory Monitoring System for Intensive Care
Bibliographic record
Abstract
The video-based non-contact respiration detection technology can be used in many application scenarios to unobtrusively and ubiquitously monitor the physical state of living beings, and various researchers are currently working on this technology. The optical flow method in tandem with crossover point method is rather effective for respiration rate extraction. However, each method has one disadvantage: 1) the redundant feature points in the traditional optical flow method increase the computational effort and reduce the estimation accuracy; and 2) the traditional crossover point method suffers from crossover points unrelated to breathing movements. For these two challenges, two optimization points are proposed 1) optimize feature point space by combining spatio-temporal information; and 2) use negative feedback design to adaptively remove crossovers unrelated to respiratory movements. The performance of the proposed algorithm is validated by the Large-scale Bedside Respiration Dataset for Intensive Care (LBRD-IC), which is established using the actual surveillance videos acquired from ICU wards. In addition, field measurements in the ICU ward have shown that our algorithm can measure respiratory signals of the single patient and multiple patients when only one surveillance camera is present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".