Muscle fatigue during assisted violin performance
Bibliographic record
Abstract
Muscle fatigue is a primary risk factor in developing musculoskeletal disorders, which affect up to 93% musicians, especially violinists. Devices providing dynamic assistive support (DAS) to the violin-holding arm can lessen fatigue. The objective was to assess DAS effects on electromyography median frequency and joint kinematics during a fatiguing violin-playing task. Fifteen university-level and professional violinists were equipped with electromyography sensors and reflective markers to record upper-body muscle activity and kinematics. They played G scales with and without DAS until exhaustion. Paired t-tests assessed DAS effects on delta (final–initial) electromyography median frequencies and joint kinematics. DAS prevented the median frequency decrease of left supraspinatus, superior trapezius, and right medial deltoid, and increases in trunk rotation, left-wrist abduction, and right arm-elevation plane. DAS effects on kinematics were marginal due to retention of musical performance despite fatigue. However, DAS reduced fatigue of several muscles, which is promising for injury prevention.Practitioner summary: Violinists are greatly affected by musculoskeletal disorders. Effects of a mobility assistive device on muscle fatigue during violin playing was investigated. The assistive technology slowed down the development of fatigue for three neck/shoulder muscles, making assisted musical performance a promising avenue to prevent violinists’ injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.003 | 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".