The Effect of Sprint and Endurance Training on Electromyogram Signal Analysis by Wavelets
Bibliographic record
Abstract
The purpose of this study was to relate the spectral changes of surface electromyograms (EMGs) to training regimes. The EMGs of M. vastus medialis and M. vastus lateralis of 8 female sprint-trained and 7 female endurance-trained athletes (ST and ET athletes) were examined while performing isokinetic knee extension on a dynamometer under 4 different loading conditions (angular velocity and load). The EMG signals were wavelet transformed, and the corresponding spectra were classified using a spherical classification, support vector machines (SVMs) and mean frequencies (MFs). Consistent differences in the EMG spectra between the 2 groups were expected because of the difference in the muscle features resulting from the various training regimes. On average, the ST athletes showed a downshift in the EMG spectra compared with the ET athletes. The spectra of the ST and ET athletes were classifiable by spherical classification and SVM but not by the MF. Thus, the different shapes of the EMG spectra contained the information for the classification. The hypothesis that specific muscle differences caused by various training regimes are consistent and lead to systematic changes in surface EMG spectra was confirmed. With the availability of new apparels, ones with integrated EMG electrodes, a measurement of the EMG will be available to coaches more frequently in the near future. The classification of wavelet transformed EMGs will allow monitoring training-related spectral changes.
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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 | 0.003 |
| 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".