Joining semantic and augmented reality to design smart homes for assistance
Bibliographic record
Abstract
INTRODUCTION: Smart homes for assistance help compensate cognitive deficits, thus favoring aging in place. However, to be effective, the assistance must be adapted to the abilities, deficits, and habits of the person. Beside the elder, caregivers are the ones who know the person's needs best. This article presents a Do-it-Yourself approach for helping caregivers designing a smart home for assistance. METHODS: A co-construction process between a caregiver and a virtual adviser was designed. The knowledge of the virtual adviser about smart homes, activities of daily living and assistance is organized in an ontology. The caregiver interacts with the virtual adviser in augmented reality to describe the home and the resident's habits inside it. The process is illustrated with an ordinary activity: 'Drink water'. RESULTS: The proposed process highlights two main steps: describing the environment and determining the resident's habits and the assistance required to improve activity performance. Visual guidance and feedback are provided to ease the process. CONCLUSION: Designing a co-construction process with a virtual adviser allows interactive knowledge sharing with the caregivers who are experts of the person's needs. Future work should focus on evaluating the prototype presented and providing deeper advice such as highlighting incomplete or incorrect scenarios, or navigation aid.
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How this classification was reachedexpand
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 | 0.004 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".