Spectral properties of myoelectric signals from different motor units in the leg extensor muscles
Bibliographic record
Abstract
Myoelectric signals measured using intramuscular electromyograms (EMGs) in animals have shown that faster motor units generate higher frequencies in their power spectra. However, evidence to relate myoelectric frequency and motor unit type from the surface electromyograms typically measured from man have remained elusive. The purpose of this study was to determine if spectral properties from surface EMG could be related to the different motor units in the muscles of the leg extensors in man. Reflex experiments (both tendon tap and electrically stimulated) and graded isometric contractions were used to generate muscle contractions with different patterns of motor unit recruitment. EMG was recorded from the vastus lateralis and medialis, rectus femoris, medial and lateral gastrocnemius and soleus muscles. The EMGs were resolved into their intensities in time-frequency space using wavelet techniques. The intensity spectra were calculated for the reflex responses and for different contractile forces. The spectra were compared using principle component analyses and ANCOVA. Electrical stimulation can result in preferentially faster motor units being recruited, and in this study resulted in higher myoelectric frequencies than for the stretch reflex. During ramped contractions the motor units are recruited in an orderly fashion from slow to fast. As the faster motor units were recruited then higher frequency components appeared within the myoelectric intensity spectra. For all muscles tested there were significant correlations between the stage in contraction and the EMG frequency. Both approaches demonstrated higher frequency components in the myoelectric spectra when the faster motor units could be assumed to be active.
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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 | 0.000 | 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) | 0.000 | 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".