Evidence of a Double Pulse Muscle Activation Strategy in Drummers’ Trunk and Upper Limb Muscles During High-velocity Cymbal Crashes
Bibliographic record
Abstract
Background: Playing the drum kit is a physically and cognitively demanding task, and skilled drummers share many such attributes with elite athletes. The ‘double pulse’ muscle activation (DPMA) pattern is a motor control strategy that has been observed in athletes of sports involving ballistic movements (e.g., baseball, golf, Mixed Martial Arts), and is believed to function to increase force transfer to the target. Objective: This study examined the muscle activation patterns of highly skilled drummers for evidence of a DPMA during high-velocity cymbal crashes. Methods: Five drummers were instrumented with electromyography electrodes on the right latissimus dorsi, triceps brachii, erector spinae, rectus abdominis, deltoideus posterior (DP), teres major, extensor carpi radialis, and flexor carpi ulnaris muscles. Six trials of data were collected, including a resting baseline, three maximum voluntary exertions (MVE) consisting of maximal effort cymbal crashes, a drumming pattern that included multiple crashes, and a ‘free-play’ trial. Results: The DPMA waveform was observed in all trials, but only those observed during the MVE trials were confirmed to coincide with the crashing movement via video analysis. The DP muscle – which functions to extend the shoulder joint to crash the stick on to the cymbal – exhibited confirmed DPMAs the most frequently. Conclusion: The extent to which drummers use the DPMA to produce high-velocity cymbal crashes within authentic playing conditions is inconclusive and needs further examination. Future study of the DPMA phenomenon in drummers would benefit from the addition of 3-dimensional motion capture to further understand the purpose of the muscle contractions of the DPMA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".