A System for Rewarding Physical and Cognitive Activity in People with Dementia
Bibliographic record
Abstract
Treatment of dementia has, until recently, largely been based on medical models that address the immediate biological needs, but often fail to meet the individualized needs of the person, including social and psychological needs. Recently, Montessori approaches have been used to validate the whole person and to provide engaging alternatives to the responsive behaviors that arise from unmet needs. In this population, sedentary lifestyle is a particularly severe problem, and there is an urgent need for more physical exercise. Given not only the known benefits of exercise, but also the difficulty of providing traditional methods of physiotherapy in the volume required, automated methods for motivating, and rewarding physical exercise are needed. The research reported in this paper focuses on the development of technologies for aging well through increased cognitive and physical activity among people with dementia. Our goal is to develop solutions for some of the issues faced by long-term care environments by creating engaging and rewarding activities that are available to people with dementia on a 24x7 basis. We have developed units called Centivizers (for “in-centivizing” behavior) that show promise in improving or maintaining physical and cognitive status in dementia by providing people with rewarding, and always-on, opportunities for engaging experiences that motivate physical exercise and cognitive activity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".