AI-Enabled Independence for Aging Populations: From Home-Based Reminder Systems to Global Assistive Technologies
Bibliographic record
Abstract
This paper presents a novel home-based reminder system designed to enhance communication and support for people living with dementia and their caregivers. Leveraging machine learning and data analytics, this system aims to provide behavioral insights that tailor the care process, promoting autonomy and independence in daily living for older adults. The research emphasizes a co-collaborative, user-informed design approach, ensuring that the technology aligns with the needs and preferences of its users. Beyond the project’s scope, this study explores the landscape of assistive technologies (ATs) for older adults, discussing the potential of artificial intelligence (AI) to revolutionize healthy aging. It highlights the importance of ethical considerations, such as privacy and user autonomy, in the development of technological solutions. The objective is to advance the dialogue on employing emerging technologies to promote sustained healthy aging, with this study serving as both a model and a broader investigation into AI's potential within assistive solutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".