Monitoring Health Status in Long Term Care Through the Use of Ambient Technologies and Serious Games
Bibliographic record
Abstract
New technologies, such as serious games and ambient activities, are being developed to address problems of under-stimulation, anxiety, and agitation in millions of people living with dementia in long term care homes. Frequent interactions with instrumented versions of these technologies may not only be beneficial for long term care residents, but may also provide a valuable new set of multifaceted data related to the health status of residents over time. In this paper, we develop a model for health monitoring in healthcare environments and we report on two studies that show how medically relevant data can be collected from elderly residents and emergency department patients in an unobtrusive way. The first study shows how data related to cognitive abilities can be collected from elderly emergency department patients and the second study shows how detailed data on a range of factors can be collected from ambient activity units designed to provide engaging interactions for long term care residents. In summary, this paper proposes the use of new technologies to transform long term care from a data poor to a data rich environment, where the health status of long term care residents and elderly patients is more closely monitored.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".