Smart homes for people with Alzheimer's disease
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
Smart home technologies constitute a potential solution to allow people with Alzheimer's disease (AD) to remain in their home. These intelligent houses contain technological devices aiming to provide adapted cognitive assistance (prompts) when needed. However, a literature review of the field revealed a predominant use of verbal prompts with little knowledge about their real effectiveness. To contribute solving this important issue, we propose, in this paper, comprehensive guidelines to help smart homes researchers to maximize the efficiency by adapting the form of prompts to the specific cognitive profiles of patients with AD. First, we identify the main deficits of AD that influence the effectiveness of prompts. Second, we details which prompting strategy to use accordingly. Third, we propose an experimental protocol, based on a well-known test, and a new prompting software, which allows to validate the proposed guidelines. Finally, we present the preliminary results of a first experiment conducted in our lab with participants having mild to moderate AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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 it