Promoting engagement in cognitively stimulating activities using an intelligent socially assistive robot
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
As the world's elderly population significantly increases, researchers are searching for ways to slow and prevent age-related diseases such as dementia. Currently, a growing body of research supports the effectiveness of using non-pharmacological interventions to reduce the decline of or improve brain functioning in people suffering from dementia. However, implementing and sustaining such interventions on a long-term basis is difficult as they require considerable resources and people, and can be very time-consuming for healthcare staff. Our research focuses on making these interventions more accessible to healthcare professionals through the aid of robotic assistants while validating their effectiveness. The objective of our work is to develop intelligent socially assistive robots as therapeutic aids designed to maintain, and even improve, the residual social and cognitive functioning in persons with dementia. In this paper, we study the social interaction attributes of our human-like socially assistive robot, Brian 2.0, during a person-centered cognitively stimulating activity to determine if the robot is able to engage a person in the activity by providing task assistance, encouragement, reinforcement, and celebration. Our preliminary proof-of-concept results show that the social interaction capabilities of Brian 2.0 are effective in engaging individuals in a cognitively stimulating game.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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