Bibliographic record
Abstract
Vital signs are a set of commonly measured signals used internationally as a baseline in medicine and surgery and are one of the most accurate predictors of clinical and physiological deterioration.Despite the clear clinical importance of vital sign measurements, there is often missed or inadequate documentation of patient vital sign measurements.The development of unobtrusive, automated and continuous monitoring offers the potential to enhance the safety and quality of patient care.This thesis details a system that uses multiple modalities to capture data and data processing techniques to extract vital signal measurements and vital signal measurement abnormalities related to subject morbidities.This thesis focuses on the examination, testing and improvement upon a current visible light video processing technique intended to extract vital signal measurements, and expand it's use to thermal infrared video vital signal extraction.Three modalities were used to gather data from healthy adult subjects and older adult inhospital patients: thermal infrared cameras, visible light cameras and pressure sensitive mats.Subjects participated in several experimental procedures including video data capture of faces, hands and feet as well as in-bed pressure mat data capture.This data was subjected to several stages of data processing to extract vital signal measurements, which include pulse, respiration temperature and mobility measurements.Data segmentation using binary masks, level set method, and watershed method were used to identify regions of interest.An adaptive spatio-temporal video processing algorithm, the main thesis contribution, was used to extract vital signal measurements.The developed algorithm was assessed for its performance in vital signal estimation, as well its
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 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".