EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling
Bibliographic record
Abstract
Glenohumeral stability is essential for a healthy function of the shoulder. It is ensured partly by the scapulohumeral muscular balance. Accordingly, modelling muscle interactions is a key factor in the understanding of occupational pathologies, and the development of ergonomic interventions. While static optimization is commonly used to estimate muscle activations, it tends to underestimate the role of shoulder’s antagonist muscles. The purpose of this study was to implement experimental electromyographic (EMG) data to predict muscle activations that could account for the stabilizing role of the shoulder muscles. Kinematics and EMG were recorded from 36 participants while lifting a box from hip to eye level. Muscle activations and glenohumeral joint reactions were estimated using an EMG-assisted algorithm and compared to those obtained using static optimization with a generic and calibrated model. Muscle activations predicted with the EMG-assisted method were generally larger. Additionally, more interactions between the different rotator cuff muscles, as well as between primer actuators and stabilizers, were predicted with the EMG-assisted method. Finally, glenohumeral forces calculated from a calibrated model remained within the boundaries of the glenoid stability cone. These findings suggest that EMG-assisted methods could account for scapulohumeral muscle co-contraction, and thus their contribution to the glenohumeral stability.
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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) | 0.001 | 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".