Context awareness architecture for ambient-assisted living applications: Case study of nighttime wandering
Bibliographic record
Abstract
OBJECTIVES: This work presents an ambient-assisted living application that encourages seniors during nocturnal wandering episodes to return to bed in calm and comfort reassurance. METHODS: Structuring knowledge by designing a software architecture capable of delivering high-level analysis and processing. A senior's home has been upgraded into a smart home enabling the gathering of habits for two weeks and set up for personalized assistance over four weeks. Home automation devices associated with Actigraph monitors and self-reported sleep were used for more accuracy. RESULTS: The architectural model can be used in ambient-assisted living applications for which data collection is permanent and continuous. Its layered organization facilitates the management of specific and general activities of daily life. The results of the home experience show that the system gave a notification whenever the need arose. On the other hand, it allowed the caregiver to get more information about the lifestyle of the senior. CONCLUSIONS: Future work should focus on providing more services to contextualize assistance. Ontology is used to structure all the ambient knowledge of the smart home. We also plan to do more home 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".