Peak activation of lower limb musculature during high flexion kneeling and transitional movements
Bibliographic record
Abstract
Few studies have measured lower limb muscle activation during high knee flexion or investigated the effects of occupational safety footwear. Therefore, our understanding of injury and disease mechanisms, such as knee osteoarthritis, is limited for these high-risk postures. Peak activation was assessed in eight bilateral lower limb muscles for twelve male participants, while shod or barefoot. Transitions between standing and kneeling had peak quadriceps and tibialis anterior (TA) activations above 50% MVC. Static kneeling and simulated tasks performed when kneeling had peak TA activity above 15% MVC but below 10% MVC for remaining muscles. In three cases, peak muscle activity was significantly higher (mean 8.9% MVC) when shod. However, net compressive knee joint forces may not be significantly increased when shod. EMG should be used as a modelling input when estimating joint contact forces for these postures, considering the activation levels in the hamstrings and quadriceps muscles during transitions. Practitioner Summary: Kneeling transitional movements are used in activities of daily living and work but are linked to increased knee osteoarthritis risk. We found peak EMG activity of some lower limb muscles to be over 70% MVC during transitions and minimal influence of wearing safety footwear.
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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) | 0.000 | 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 | 0.000 | 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".