An ambient assisted living nighttime wandering system for elderly
Bibliographic record
Abstract
The Assistive living technologies provide good results for the support of specific activities transforming a home into a smart home. In this paper, we present a personalized ambient support system for elderly suffering from Alzheimer's dementia and nighttime wandering. Our goal is to help the person stay at home as long as possible and regain a regular circadian cycle while providing more comfort to the caregiver. The intervention proceeds in two phases. During the monitoring phase, the system determines the resident profile based on nighttime routines. Data is gathered from sensors dispatched in the smart home, coupled with physiological data obtained from worn sensors. Data is then classified to determine engine rules that will provide assistance to the resident to satisfy his needs. In the second phase, assistance is provided to the person by triggering rules depending on the activities occurring during night. It offers a calm environment with music and visual icons to soothe the person then encourage it to return to bed. The system is installed at the Alzheimer's home using wireless technologies. Multiple heterogeneous technologies are put in common to achieve it. Reliabilities and robustness tests were carried out in a 4 1/2 room apartment for 3 months with over 3.78 million collected data tuples. These tests have established three clusters of activities necessary for the recognition of nighttime wandering activities. This helped start an ongoing experiment in homes.
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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 | 0.000 |
| 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) | 0.002 | 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".