Designing calm and non-intrusive ambient assisted living system for monitoring nighttime wanderings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Assistive living technologies provide support for specific activities, transforming a home into a smart home. The purpose of this paper is to present how to design, implement, deploy and install a personalized ambient support system for the elderly suffering from Alzheimer’s disease (AD) and nighttime wandering. Design/methodology/approach The intervention presented in this paper proceeds in two phases. During the monitoring phase, the system determines the profile of the person with AD, based on nighttime routines. Data are gathered from sensors dispatched in the smart home, coupled with physiological data obtained from sensors worn by the person. Data are then classified to determine engine rules that will provide assistance to the resident to satisfy their needs. During the second phase, smart assistance is provided to the person via environmental cues by triggering rules based on the person’s habits and the activities occurring during night. Findings The paper develops the architecture of a non-intrusive system that integrates heterogeneous technologies to provide a calm environment during night and limit wandering periods. Practical implications The goal is to help people age well at home as long as possible and recover a regular circadian cycle while providing more comfort to the caregiver. Originality/value The system presented in this paper offers a calm and personalized environment with music and visual icons to soothe persons with AD and encourage them to go back to bed. It is installed at the patient’s home using wireless technologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it