How Do Violinists Adapt to Dynamic Assistive Support? A Study Focusing on Kinematics, Muscle Activity, and Musical Performance
Bibliographic record
Abstract
Objective Assessing violinists’ motor and musical performance adaptations to dynamic assistive support (DAS) provided by a passive device, using a force-field adaptation paradigm. Background Up to 93% of instrumentalists are affected by musculoskeletal injuries and particularly violinists. The repetitive nature of their work may lead to muscle fatigue, an injury risk factor. DAS has been used in occupational settings to minimize muscle activations and limit fatigue accumulation. DAS may however affect motor and musical performance. Method Fifteen expert violinists were equipped with reflective markers and surface and intramuscular electromyography (EMG) sensors. Movements, muscle activations, and sound were recorded while participants completed three experimental conditions for which they continuously played a 13-s musical excerpt: Control (no DAS), Adaptation (DAS), and Washout (no DAS). DAS was applied at the left elbow (violin-holding side). Conditions were repeated 1 week later. Participants later listened to their own audio recordings playing with and without DAS and blindly assessed their performances. Linear mixed models were used to compare DAS and no-DAS conditions’ kinematic, EMG, and musical performance data. Results DAS perturbed user kinematics but reduced mean activations of left medial deltoid and superior trapezius. Joint kinematic and muscle activation patterns between DAS and no DAS conditions however remained similar. Musical performance was unchanged with DAS. Conclusion Though DAS modified violinists’ upper-limb configurations, resulting kinematics were not detrimental to musical performance. Reduced muscle activations with DAS could contribute to lessening muscle fatigue. Application Although its effect on muscle fatigue should be further investigated, DAS might be useful in preventing violinists’ injuries.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".