Comparisons of mechanical and electromyographical muscular utilization ratios.
Bibliographic record
Abstract
The physical loading of a muscle during functional activities can be estimated by the muscular utilization ratio. This ratio is defined as the percentage of muscular involvement relative to the maximal capacity. Either mechanical or electromyographical approaches can be used to obtain the muscle utilization ratio. However, the non-linear relationship between electromyographical activity and muscle force, as well as the non-equivalence between agonist muscles, may create differences between the mechanical muscle utilization ratio calculated from joint moments and the electromyographical muscle utilization ratio calculated from electromyographical data. The aim of this study was to compare, during a squat test, the mechanical muscle utilization ratio and the electromyographical muscle utilization ratio estimated by three different methods; direct linear approximation, second order polynomial regression and linear interpolation. The knee extensor moment and electromyographical data of rectus femoris and vastus medialis of 11 subjects were recorded during both knee extension and squat. Both tests were performed with the knee maintained at 90 degrees of flexion. The results showed that: a) the electromyographical muscle utilization ratio, calculated from the average of vastus medialis and rectus femoris, significantly underestimates the mechanical muscle utilization ratio (ANOVA, p < 0.01), b) the differences between the mechanical muscle utilization ratio and the electromyographical muscle utilization ratio are larger for the direct linear approximation method than for the second order polynomial regression (ANOVA, p < 0.01) or the linear interpolation method (ANOVA, p < 0.01), and c) independent of the method utilized, there is no difference between the electromyographical muscle utilization ratio predicted by the vastus medialis as compared with the rectus femoris (ANOVA, p > 0.01).(ABSTRACT TRUNCATED AT 250 WORDS)
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".