Bow-Side Kinematics Studies in Violinists: An Experimental Design Tracking Intra- and Inter-Musician Variability by Bow Stroke, String Played, and Tempo
Bibliographic record
Abstract
Comparison of bow-side kinematics in violinists is hindered by the scarcity of studies available. This makes meta-analysis impossible. This paper assesses the effect of music-based variables (bow stroke, tempo, and string played) on intra- and inter-participant variability in joint kinematics. The joint kinematics of nine high-level violinists were acquired via a motion capture system while they played a standardized piece of music involving contrasting bow strokes and strings at different tempi. Results were compared using linear mixed models using the root mean square (RMS) for each joint. We found highly individualized patterns of play, deduced from a low intra- but high inter-musician variability (4.2° vs 13.1° of normalized RMS) in joint kinematics. String played and bow stroke had the greatest effect on joint kinematics. The string played had the greatest impact on shoulder kinematics, and the bow stroke had the greatest impact on elbow and wrist kinematics. Based on these results, we propose guidelines for future research designed to study bow kinematics in the field of biomechanics of violin movements. For ease of comparison between studies and to limit the time and resources required, our main suggestions are to use repeated measures designs with a legato reference condition and to choose pieces of music spanning multiple strings.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".