Towards Aging-in-Place: Automatic Assessment of Product Usability for Older Adults with Dementia
Bibliographic record
Abstract
Considerations of how to facilitate aging-in-place are becoming increasingly pertinent as caregivers are overwhelmed by an aging population. A primary challenge to independent living is the inability to use products associated with tasks of daily living. As improving the usability of these products for the elderly will extend their independence, this work attempts to automate and expedite the assessment of usability using artificial intelligence. Video analysis is performed to temporally segment video of human-product interaction and automatically identify segments in which the human has difficulty operating the product. The approach is applied to the study of water faucet designs for older adults with dementia. Empirical analysis is performed on videos of dementia patients operating various faucet types, demonstrating the accuracy of the temporal segmentation (88.1%) and the ability to estimate the relative advantage of either design in terms of operational ease.
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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 | 0.000 | 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 | 0.000 |
| 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".