MEDARM: a rehabilitation robot with 5DOF at the shoulder complex
Bibliographic record
Abstract
A key approach for reducing motor impairment and regaining independence after stroke is frequent and repetitive functional training. A number of robotic devices have been developed to assist therapists with the labourious task of providing treatment. Although robotic technology is showing significant potential, its effectiveness for upper limb rehabilitation is limited in part by the inability to make functional reaching movements. A major contributor to this problem is that current robots do not replicate motion of the shoulder girdle despite the fact that the shoulder girdle plays a critical role in stabilizing and orienting the upper limb during activities of daily living. To address this issue, a new adjustable robotic exoskeleton called MEDARM is proposed for motor rehabilitation of the shoulder complex. MEDARM provides independent control of six degrees of freedom (DOF) of the upper limb: two at the sternoclavicular joint, three at the glenohumeral joint and one at the elbow. Its joint axes are optimally arranged to mimic the natural upper- limb workspace while avoiding singular configurations and while maximizing manipulability. This mechanism also permits reduction to planar shoulder/elbow motion in any plane by locking all but the last two joints. Electric motors actuate the joint using a combination of cable and belt transmissions designed to maximize the power-to-weight ratio of the robot while maintaining backdriveability and minimizing inertia. Thus, the robot can provide any level of movement assistance and gravity compensation. This paper describes the proposed technical design for MEDARM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".