Designing a Robotic Exoskeleton for Shoulder Complex Rehabilitation
Bibliographic record
Abstract
Frequent and repetitive functional training of the upper limb is a key aspect of regaining independence after stroke. Traditionally, this is achieved through manual one-on-one therapy, but patients are often unable to get sufficient treatment due to budget and scheduling constraints. An ideal solution may be robotic therapy, which is becoming an increasingly viable tool. Unfortunately, current rehabilitation robots ignore shoulder girdle motion, even though it plays a critical role in stabilizing and orienting the upper limb during everyday movements. To address this issue, a new adjustable robotic exoskeleton is proposed that provides independent control of six degrees of freedom 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 natural upper-limb range of motion without reaching singular configurations and while maximizing manipulability across the workspace. This joint configuration also permits reduction to planar shoulder/elbow motion in any plane by locking all but the last two joints. Electric motors actuate the mechanisms using cable and belt transmissions designed to maximize the load capabilities of the robot while maintaining backdriveability and minimizing inertia. The device will be able to operate both as an assessment tool and as a therapy tool by monitoring and assisting movements. It will also be able to provide any level of gravity compensation. Controlling the entire shoulder complex facilitates training with more natural movements, with the added benefit of gaining the ability to observe and prevent compensatory motion.
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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) | 0.000 | 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 | 0.001 | 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".