EMG Burst and Gap Comparisons in Muscles of the Upper and Lower Limbs
Bibliographic record
Abstract
Portable electromyography (EMG) equipment enables muscle activity to be measured during activities of daily living. Patterns of EMG activity have been quantified through analysis of frequency and amplitude during periods of EMG quiescence (gaps) and activity (bursts). Research suggests that during daily activity in young adults upper limb muscles burst more frequently, but at lower amplitudes relative to lower limb muscles. PURPOSE: To compare EMG burst and gap activity between the biceps brachii (BB), triceps brachii (TB), vastus lateralis (VL), and biceps femoris (BF) for a typical 8 hour day. METHODS: The young and old men and women (n=15 women, n=16 men) were recreationally active, and questionnaires showed activity scores did not differ (P=0.1). Surface EMG was recorded (Biometrics, Gwent, UK) for the BB, TB, VL, and BF. Signals were sampled at 1000HZ, imported into Spike 2 Version 5 (CED, Cambridge UK) for custom script analysis. Bursts were defined as activation >2% MVE, and gaps as <1%MVE both lasting longer than 0.1s. RESULTS: Burst number was higher in the BB compared with the TB (33%Δ; P=0.011) and higher in the BF compared with the VL (39%Δ P=0.004). Burst number did not differ between upper and lower limb muscles. Burst amplitude recorded in the VL (9.13 + 3.06 %MVE) and the BF (9.45 + 2.89 %MVE) was nearly double the BB (5.48 + 1.39 %MVE) and the TB (4.85 + 0.67 %MVE). The number of gaps did not differ between upper and lower limb muscles, but mean gap duration was higher in the upper limbs (P=0.002), whereas gap amplitude was lower in the upper limb muscles (−7%Δ; P<0.05) relative to the two lower limb muscles. CONCLUSIONS: Burst activity was higher in flexor muscles compared with extensor muscles. Burst and gap number did not differ between upper and lower limb muscles, yet the lower limb muscles relative to the upper limb muscles exhibited greater burst amplitude and gap amplitude.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".