Motor unit recruitment patterns 2: the influence of myoelectric intensity and muscle fascicle strain rate
Bibliographic record
Abstract
To effectively meet the force requirements of a given movement an appropriate number and combination of motor units must be recruited between and within muscles. Orderly recruitment of motor units has been shown to occur in a wide range of skeletal muscles, however, alternative strategies do occur. Faster motor units are better suited to developing force rapidly, and produce higher mechanical power with greater efficiency at faster shortening strain rates than slower motor units. As the frequency content of the myoelectric signal is related to the fibre type of the active motor units, we hypothesised that, in addition to an association between myoelectric frequency and intensity, there would be a significant association between muscle fascicle shortening strain rate and myoelectric frequency content. Myoelectric and sonomicrometric data were collected from the three ankle extensor muscles of the rat hind limb during walking and running. Myoelectric signals were analysed using wavelet transformation and principal component analysis to give a measure of the signal frequency content. Sonomicrometric signals were analysed to give measures of muscle fascicle strain and strain rate. The relationship between myoelectric frequency and both intensity and muscle fascicle strain rate was found to change across the time course of a stride, with differences also occurring in the strength of the associations between and within muscles. In addition to the orderly recruitment of motor units, a mechanical strategy of motor unit recruitment was therefore identified. Motor unit recruitment is therefore a multifactorial phenomenon, which is more complex than typically thought.
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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 | 0.001 | 0.004 |
| 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".