Bed occupancy monitoring: Data processing and clinician user interface design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unobtrusive and continuous monitoring of patients, especially at their place of residence, is becoming a significant part of the healthcare model. A variety of sensors are being used to monitor different patient conditions. Bed occupancy monitoring provides clinicians a quantitative measure of bed entry/exit patterns and may provide information relating to sleep quality. This paper presents a bed occupancy monitoring system using a bed pressure mat sensor. A clinical trial was performed involving 8 patients to collect bed occupancy data. The trial period for each patient ranged from 5-10 weeks. This data was analyzed using a participatory design methodology incorporating clinician feedback to obtain bed occupancy parameters. The parameters extracted include the number of bed exits per night, the bed exit weekly average (including minimum and maximum), the time of day of a particular exit, and the amount of uninterrupted bed occupancy per night. The design of a clinical user interface plays a significant role in the acceptance of such patient monitoring systems by clinicians. The clinician user interface proposed in this paper was designed to be intuitive, easy to navigate and not cause information overload. An iterative design methodology was used for the interface design. The interface design is extendible to incorporate data from multiple sensors. This allows the interface to be part of a comprehensive remote patient monitoring system.
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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 | 0.001 |
| 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 it