Neuromuscular Strategies during Cycling at Different Muscular Demands
Bibliographic record
Abstract
PURPOSE: This study investigated muscle coordination while pedaling at 150 and 300 W with a cadence of 90 rpm. Changes in the variability of the electromyographic (EMG) signals were quantified in 14 subjects. METHODS: Principal component analysis was used to find correlated EMG patterns among seven leg muscles that reflect neuromuscular strategies while pedaling. Sample entropy was used to assess the regularity of the short-term fluctuations of the EMG. Signal structure relates to the autocorrelation and to the information in the phase of the signal. This study used the information encrypted in the phase to quantify neuromuscular control and compared the results to phase-randomized surrogate data. RESULTS: Although the pattern remained similar, the correlation between individual muscles showed effort-dependent differences. Increased workload altered the overall neuromuscular strategy indicated by changes in the contribution of individual muscles to the movement. Additionally, the executed strategy was characterized by increased structure. Regularity of the short-term fluctuations in the EMG increased significantly with effort level. Both experimental conditions showed more structure in the phase of the EMG compared to the surrogate data. CONCLUSIONS: This increased structure in the EMG signal may represent a less random and more orderly recruited firing pattern during the pedaling task at higher effort levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".