Using Unmanned Aerial Vehicles (UAVs) in locating wandering patients with dementia
Bibliographic record
Abstract
This paper presents findings from three experiments involving the use of Unmanned Aerial Vehicles (UAVs) for the purpose of locating a wandering person whose behavior resembles the behavior of a wandering patient with dementia. Additionally, it presents research review on the use of UVs in locating wandering eprsons with dementia. The characteristics of this critical form of wandering-or eloping - are discussed. By using test subjects simulating individual lost patients with dementia, along with current Search and Rescue (SAR) operational methods, experiments were performed employing drones to find the wandering persons. The algorithm used to determine the drone paths is based on the analysis of incidents analyzed in the literature from the International Search and Rescue Incident Database (ISRID) which contains thousands of international and national police records on lost persons. The experiments revealed that UAVs, if used with the pre-determined path, could expedite the search process thus improving the survivability of the lost person. The paper considers the time needed to detect the person, duration of the complete mission, the differential longitude and latitude analysis from an Initial Planning Point (IPP), the time taken to find the test subject and the battery life of the drone. Challenges and recommendations are presented to inform future experiments.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".