Torso and Bowing Arm Three-Dimensional Joint Kinematics of Elite Cellists: Clinical and Pedagogical Implications for Practice
Bibliographic record
Abstract
BACKGROUND: Elite cello playing requires complex and refined motor control. Cellists are prone to right shoulder and thoracolumbar injuries. Research informing injury management of cellists and cello pedagogy is limited. The aims of this study were to quantify the torso, right shoulder, and elbow joint movement used by elite cellists while performing a fundamental playing task, a C major scale, under two volume conditions. METHODS: An eight degrees of freedom upper limb biomechanical model was applied to 3D motion capture data of the torso, upper arm, and forearm for 31 cellists with a mean experience of 19.4 yrs (SD 9.1). Two-factor ANOVA compared the joint positions between the four cello strings and two volume conditions. FINDINGS: Significant (p<0.05) effects were found for either the string and/or volume conditions across all torso, shoulder, and elbow joint degrees of freedom. The torso was consistently positioned in left rotation from 5.0° (SD 5.6) at the beginning of the scale, increasing to 16.3° (5.5) at its apogee. The greatest mean shoulder flexion, internal rotation, and abduction joint angles were observed when playing at the tip of the bow on the top string (A): 107.2° (11.6), 59.1° (7.1), and -76.9° (15.7), respectively, during loud playing. INTERPRETATION: Elite cellists use specific movement patterns to achieve string crossings and volume regulation during fundamental playing tasks. Implications of the static left-rotated torso posture and high degrees of combined shoulder flexion and internal rotation can be used to inform clinical and pedagogical practices.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".