Automated hand hygiene monitoring system using imagery and bluetooth low energy sensors
Bibliographic record
Abstract
This thesis designs and implements a hand hygiene monitoring system using Bluetooth low energy and imagery sensors. As the cost of treating healthcare-associated infections increases, the need for monitoring and improving hand hygiene compliance percentages for healthcare providers increases. Several techniques for hand hygiene compliance monitoring exist, but it was found that electronic automated systems are the most reliable solution because they provide more accurate continuous compliance measurements for lower cost. Other similar systems based on a variety of technologies exist, however, they are either uniquely evidence based, so that they capture hygiene moments and apply a statistical model for hygiene opportunities, and they, therefore, do not provide real-time information; or they require human interference to determine compliance rendering them not fully automated. In this thesis, available monitoring techniques, focusing on automated electronic systems, are first introduced. Then, a novel automated hand hygiene monitoring system, capable of capturing hygiene moments with more than 90% precision, is proposed. The proposed system was first tested in a lab environment with private rooms setup, the system was also tested in semi-private rooms setup and then implemented in the Hematology and Oncology Department at the Health Sciences Center of Eastern Health for a pilot study. The study showed a high correlation between the compliance rates calculated by the proposed system compared to the compliance rates found by direct observers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".