Preliminary results for measurement and classification of overnight wandering by dementia patient using multi-sensors
Bibliographic record
Abstract
The measurement and detection of overnight wandering is a significant issue for dementia patients and their caregivers such as a spouse. The wandering places the patient at risk of injury or even death if they fall or leave their residence without being detected. While it also causes stress and reduced sleep for the caregiver as they try to remain alert to the actions of their partner. This paper presents initial data for the first participant from an ongoing study of dementia patients where a wander detection and diversion system based on low-cost commercial sensors has been deployed into the residence. The paper shows that over a 3-week period, the analysis and classification of the sensor data is able to measure the behavior of the patient. In this period, the patient only used the washroom overnight and did not wander into other parts of the residence. These early results show that an off the shelf system targeted for residential security and home automation applications based on low-cost sensors supported with automated analysis and classification has the potential to be used to assist caregivers and dementia patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".