Effect of Expertise on Shoulder and Upper Limb Kinematics, Electromyography, and Estimated Muscle Forces During a Lifting Task
Bibliographic record
Abstract
OBJECTIVE: To highlight the working strategies used by expert manual handlers compared with novice manual handlers, based on recordings of shoulder and upper limb kinematics, electromyography (EMG), and estimated muscle forces during a lifting task. BACKGROUND: Novice workers involved in assembly, manual handling, and personal assistance tasks are at a higher risk of upper limb musculoskeletal disorders (MSDs). However, few studies have investigated the effect of expertise on upper limb exposure during workplace tasks. METHOD: Sixteen experts in manual handling and sixteen novices were equipped with 10 electromyographic electrodes to record shoulder muscle activity during a manual handling task consisting of lifting a box (8 or 12 kg), instrumented with three six-axis force sensors, from hip to eye level. Three-dimensional trunk and upper limb kinematics, hand-to-box contact forces, and EMG were recorded. Then, joint contributions, activation levels, and muscle forces were calculated and compared between groups. RESULTS: Sternoclavicular-acromioclavicular joint contributions were higher in experts at the beginning of the movement, and in novices at the end, whereas the opposite was observed for the glenohumeral joint. EMG activation levels were 37% higher for novices but predicted muscle forces were higher in experts. CONCLUSION: This study highlights significant differences between experts and novices in shoulder kinematics, EMG, and muscle forces; hence, providing effective work guidelines to ensure the development of a safe handling strategy is important. APPLICATION: Shoulder kinematics, EMG, and muscle forces could be used as ergonomic tools to identify inappropriate techniques that could increase the prevalence of shoulder injuries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".