A high-level controller for robot-assisted rehabilitation based on Chedoke-McMaster stroke assessment
Bibliographic record
Abstract
Amongst the major challenges in post-stroke rehabilitation are the repetitiveness nature of rehabilitation procedure, and the accessibility of therapists for long-term treatment. In manual rehabilitation procedure, the patient is subjected to repetitive mechanical movement of the affected limb by the therapist. In one of the techniques called active-assist exercise, the subject moves his affected limb along a specified trajectory with the therapist guiding the motion. The therapist gives some assistance to the subject to complete the course if deemed necessary and the procedure repeats.. The significant advantages of using robots in assisting rehabilitation are its efficiency and it is fatigue free. The robots however need to be developed to have the capability of human therapist in providing the rehabilitation more naturally. In this paper, the research work focuses on developing a new framework for the robot controller system. In particular, a high-level controller, which is in the form of supervisory controller based on discrete event system theory, is discussed. The controller is capable of giving complex, autonomous guidance during the therapeutic procedure naturally based on the Chedoke-McMaster stroke assessment method.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".