Muscle Conduction Velocity Estimation Using High Density Electromyography
Bibliographic record
Abstract
Overall conduction velocity (CV) in a population of activated muscle fibres is related to the state of the muscle and can be estimated from the electromyogram (EMG). CV estimation is affected by the distance between electrodes of a bipolar pair (inter-electrode distance or IED), and the distance between two bipolar pairs used to detect conduction delay (inter-signal distance or ISD). Reported CV’s are generally in the range of 3.5 – 5 m/s [Farina-MBEC-2001; Beck-JEK-2004]. The purpose of this study was to examine the effect on CV estimates of IED and ISD, contraction level, and muscle length. EMG data were recorded from the long and short heads of the biceps brachii (BBL and BBS), and brachioradialis (BR), of five subjects, using monopolar high-density EMG electrodes. Subjects performed elbow flexion contractions at three force levels (30, 40 and 50% maximum), and three elbow joint angles (60, 90 and 120 degrees). CV estimates were computed for different bipolar electrode configurations; estimates corresponding to published values were obtained for IED=15 mm and ISD=20 mm. ANOVA analysis of CV values revealed that contraction level had no significant effect (p-values: 0.336 to 0.774), and CV varied significantly with joint angle only for the highest contraction level in BBS (p=0.01). Values for all contraction levels were then grouped; ANOVA analysis showed that CV varied significantly with muscle (p<0.00002) for all joint angles. These results are analysed with regard to the nature of the EMG signal, and how the signal changes with contraction level and joint angle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".