The development of an upper limb stroke rehabilitation robot: identification of clinical practices and design requirements through a survey of therapists
Bibliographic record
Abstract
PURPOSE. Timely and adequate rehabilitation after a stroke is crucial to maximising recovery. A way of increasing treatment access could be through robots, which would aid therapists in providing post-stroke rehabilitation. This research sought to discover the needs and preferences of therapists with respect to a robot that focuses on upper limb rehabilitation. Understanding requirements for devices could help to increase integration into clinical practice. METHODS. An international online survey was distributed through professional organisations and e-mail list services to therapists. The survey contained 85 items covering topics such as therapist background and treatment approach, rehabilitation aims and robotic rehabilitation device attributes. RESULTS. Data were analysed for 233 respondents, most of whom were physiotherapists and occupational therapists from Australia, Canada and USA. Top attributes included: facilitating a variety of arm movements, being usable while seated, giving biofeedback to clients, having virtual activities specific to daily living, being useful in-home and having resistance adjustable to client needs. In addition, the device should cost under 6000 USD. CONCLUSIONS. Findings from this survey provide guidance for technology developers regarding therapists' specifications for a robotic device for upper limb rehabilitation. In addition, findings offer a better understanding of how acceptance of such devices may be facilitated.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".