Wavelet and principal component analysis of electromyographic activity and slow component of oxygen uptake during heavy and severe cycling exercise
Bibliographic record
Abstract
The aim of the study was to investigate whether the slow component of oxygen uptake was concurrent with the recruitment of large α-motoneuron muscle fibres by using wavelet and principal component analysis (PCA) of electromyography (EMG) during heavy and severe cycling exercise. Eleven male subjects participated in the study. After establishing each subject’s maximum value of oxygen uptake through an incremental test on the cycle ergometer, the subjects performed 6-min cycling tests at heavy and severe intensity. EMG signals were collected from rectus femoris, biceps femoris long head, tibialis anterior, and medial gastrocnemius and processed by combined use of wavelet and PCA analysis. The time delays to the onset of slow component occurred significantly earlier during severe (105.22 ± 5.45 s) compared with during heavy (138.78 ± 15.09 s) exercise. ANOVA with repeated measures showed that for all muscles tested, the angle θ formed by the first and second principal components decreased significantly between time windows during heavy and severe exercise. However, significant increases of EMG mean power frequency (MPF) were found only during heavy exercise. Our results show the concurrence of the oxygen uptake slow component with the additional recruitment of muscle fibres, presumably less efficient large α-motoneuron fibres. Novelty The expected rise in MPF may be offset by muscle fatigue occurring in the later time windows of the slow component during severe exercise. The gradual shift to higher EMG frequencies throughout the slow-component phase was reflected in the progressive and significant decrease of angle θ.
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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 | 0.001 |
| 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".