Evaluating the Relationship Between Surface and Intramuscular-Based Electromyography Signals: Implications of Subcutaneous Fat Thickness
Bibliographic record
Abstract
Intramuscular (iEMG) and surface electromyographic (sEMG) signals have been compared previously using predictive regression equations, finite element modeling, and correlation and cross-correlation analyses. Although subcutaneous fat thickness (SCFT) has been identified as a primary source of sEMG signal amplitude attenuation and low-pass filter equivalence, few studies have explored the potential effect of SCFT on sEMG and iEMG signal characteristics. The purpose of this study was to investigate the relationship between normalized submaximal iEMG and sEMG signal amplitudes collected from 4 muscles (rectus femoris, vastus lateralis, infraspinatus, and erector spinae) and determine whether SCFT explains more variance in this relationship. The effect of sex was also explored. Linear regression models demonstrated that the relationship between sEMG and iEMG was highly variable across the muscles examined (adjusted coefficient of determination [Adj R2] = .02-.74). SCFT improved the model fit for vastus lateralis, although this relationship only emerged with the inclusion of sex as a covariate. Thus, this research suggests that SCFT is not a prominent factor affecting the linearity between sEMG and iEMG. Researchers should investigate other parameters that may affect the linearity between sEMG and iEMG signals.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".