Task-Oriented Control of Muscle Coordination during Cycling
Bibliographic record
Abstract
PURPOSE: This study investigated the effects of different biomechanical constraints on the variability of muscle activation during cycling. METHODS: Fifteen male athletes cycled at a power of 150 and 300 W. Surface EMG was recorded from seven lower limb muscles. Wavelet transformed EMG signals of all muscles were subjected to a principal component analysis to study the variability of the EMG. The full vector space was reduced to the first principal components that explained 90% of the variance. The input data of each cycle revolution were projected onto these principal component vectors. Means and SD of the projections were calculated across all cycles and summed across all time points. The relative variability (RV) was expressed as the ratio between the SD and the mean of the summed projections. The principal angle was calculated between the principal components used for the 150-W condition and those used for the 300-W condition. RESULTS: The RV could be split into low- and high-variability components. The variability was smaller for the lower ordered eigenvectors compared with the higher ordered ones (P < 0.001) independent of the loading condition. Overall, the 300-W condition showed lower RV compared with the 150-W condition (P < 0.01). The average principal angle between the 150- and 300-W subspaces was 0.4, respectively. CONCLUSIONS: Structured aspects of variability were found in the muscle activation of lower leg muscles during cycling. In the context of the minimum intervention principal, this might be interpreted as a transition into a regime that requires specific necessary muscles where the increased constraints of the task specify the muscle coordination pattern in a more precise way.
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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 | 0.002 |
| 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) | 0.001 | 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".