Patient Mobility Monitoring Through the Analysis of Bed Exit Centre of Pressure
Bibliographic record
Abstract
The increase in the older adult population and the benefits of independent living are leading to the introduction of new approaches for monitoring the health and well-being of seniors living in their own homes.Technologies that can monitor an older person's daily activities and detect changes in functionality have the potential to act as early warning systems so that help can be provided before a serious health event occurs.Home monitoring can contribute to the creation of more supportive environments for aging at home.This thesis uses a pressure-sensitive mat that is placed between a bed frame and the mattress.The pressure data can reveal important clinical information about the bed occupant.This thesis deals with the analysis of bed exit characteristics in terms of centre of pressure trajectory and its dynamic behaviour.The algorithms were tested on data collected from bed occupants to demonstrate the various clinical features.A graphical user interface (GUI) was designed to assist health care providers in interpreting and comparing these clinical features.This work will be used to monitor frail older adults in their own homes to assist with early detection of mobility decline.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".