Smart home technology solution for night-time wandering in persons with dementia
Bibliographic record
Abstract
INTRODUCTION: More than half of persons with dementia will experience night-time wandering, increasing their risk of falls and unattended home exits. This is a major predictor of caregiver burnout and one of the major causes of early institutionalization. METHODS: Using smart home technologies such as sensors, smart bulbs, pressure mats and speakers, the Night-time Wandering Detection and Diversion system is designed to assist caregivers and persons with dementia that are at risk of wandering at night. Being placed in homes around Ottawa for a 12-week trial, the system allows caregivers to rest peacefully in the night, as it detects when the person with dementia gets out of bed and automatically provides cue lighting to guide them safely to the washroom. The system also uses prerecorded audio prompts, if they venture from the bedroom, only waking the caregiver when the person with dementia opens an exit door. RESULTS: Thus far, the average depression and anxiety in caregivers have been improved after the 12 weeks, and most have said that they sleep more peacefully. CONCLUSION: The system has proven successful in supporting the safety of persons with dementia as well as their caregivers.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".