Development of a Cadaveric Shoulder Motion Simulator with Open-Loop Iterative Learning for Dynamic, Multiplanar Motion: A Preliminary Study
Bibliographic record
Abstract
Ex vivo shoulder motion simulators are commonly used to study shoulder biomechanics but are often limited to performing simple planar motions at quasi-static speeds using control architectures that do not allow muscles to be deactivated. The purpose of this study was to develop an open-loop tendon excursion controller with iterative learning and independent muscle control to simulate complex multiplanar motion at functional speeds and allow for muscle deactivation. The simulator performed abduction/adduction, faceted circumduction, and abduction/adduction (subscapularis deactivation) using a cadaveric shoulder with an implanted reverse total shoulder prosthesis. Kinematic tracking accuracy and repeatability were assessed using maximum absolute error (MAE), root mean square error (RMSE), and average standard deviation (ASD). During abduction/adduction and faceted circumduction, the RMSE did not exceed 0.3, 0.7, and 0.8 degrees for elevation, plane of elevation, and axial rotation, respectively. During abduction/adduction, the ASD did not exceed 0.2 degrees. Abduction/adduction (subscapularis deactivation) resulted in a loss of internal rotation, which could not be restored at low elevation angles. This study presents a novel control architecture, which can accurately simulate complex glenohumeral motion. This simulator will be used as a testing platform to examine the effect of shoulder pathology, treatment, and rehabilitation on joint biomechanics during functional shoulder movements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".