Developing an Android App for Dementia Patient Location: Prevention of Wandering Case Study
Bibliographic record
Abstract
In dementia patients, wandering means acting on a desire to be elsewhere.Wandering can be dangerous.According to the Alzheimer's Association Safety Center Six in 10 people with dementia will wander and become lost; many do so repeatedly 1 .Most caregivers prefer to use tracking technologies as a back-up to other strategies of management, particularly supervision by a caregiver and locked doors.In cases where the risk of harm from getting lost to be low, tracking is used to preserve the independence of the patient with dementia.Various efforts have been attempted to attain a better understanding of mobility behavior of the dementia patients, but most studies are based on institutionalized patients and the assessment usually relies on reports of caregivers and institutional staff, using observational approaches, activity monitoring, or behavioral checklists.However, there are many current technologies that uses GPS devices to find the precise location on a map of a person equipped with these devices (e.g.bracelet).However, to date, research into dementia patient activities, using simple mobility tracking technologies has been largely limited to studies tracing of the routes of patients using the patient smart phone.The objective of this case study is to use the smart watch with the mobile phone along with messaging push technologies for alerting the patient and the caregiver and redirect the patient in case of wandering outside the prescribed region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".