Perception of the usefulness of socially assistive robots for adherence to home-based rehabilitation exercises for persons with chronic neurological conditions
Bibliographic record
Abstract
Adherence to home-based rehabilitation exercises is a challenge for individuals with chronic neurological conditions. Socially assistive robots are becoming an option to resolve compliance challenges with rehabilitation exercises. An in-depth exploration of how natural human-robot interaction could help improve adherence to home-based rehabilitation exercises is justified. The first study objective was to explore how a robot that offers supervision and encouragement could increase adherence to home-based long-term rehabilitation exercises for individuals with neurological conditions. The second objective was to explore perceived obstacles and facilitators related to using a robot with artificial audition capabilities. These results will be used to guide the design and optimization of robot audition technology within a larger research program. Six focus groups were held to elicit the views of individuals with neurological conditions (n=3 groups) and health care professionals (n=3 groups). Content was analyzed qualitatively. Four topics were addressed during the focus groups: challenges in performing exercises, needs to be met by the technology, desired technological characteristics and anticipated impacts. Our results identified different needs, characteristics and anticipated limitations as preliminary key items to guide a user-centered design. Participants were generally positive about the concept of using socially and technically assistive robotic technology to meet the home-based exercise needs of people with neurological conditions. Health care professionals, however, anticipated more limitations than clients.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".