The influence of muscle action on joint loading during dynamic finger pressing tasks in an open-source modelling environment
Bibliographic record
Abstract
Assessing finger joint loading is essential to the prevention of work-related musculoskeletal disorders of the hand and fingers. This study aimed to evaluate the effect of muscle action on joint loading during dynamic finger pressing tasks using an open-source musculoskeletal modelling platform. Eight participants performed submaximal dynamic index finger pressing tasks while maintaining a nominal vertical target force of 10 N. Kinematic and force data were used to develop and assess an inverse dynamics link segment model and a musculoskeletal model. Although musculoskeletal model results for metacarpophalangeal joint compression evaluated statically (34.92 ± 1.53 N) and dynamically (34.80 ± 4.40 N) did not differ, changes in anterior-posterior shear and compression throughout the motions identify where the current open-source model is able to provide valuable insight into the assessment of risk of developing MSD during sub-maximally loaded dynamic pressing tasks.
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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 | 0.003 |
| 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 | 0.001 |
| 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".