Wrist Internal Loading and Tempo-Dependent, Effort-Reducing Motor Behaviour Strategies for Two Elite Pianists
Bibliographic record
Abstract
One of the greatest challenges in reducing high rates of performance injuries among musicians is in providing them usable tools to address playing-related musculoskeletal problems (PRMP) before they become disorders. Studies in biomechanics have the potential to provide such tools. In order to better understand the mechanisms through which PRMP manifest in pianists, especially in the distal segments of the upper limbs, the current study quantifies wrist internal loading (WIL) and wrist impact loading frequency. It does so while discussing pianists' motor behaviours and observed effort-reduction strategies in the wrists as a function of anthropometry. This concept has great utility for performers. A VICON 3D motion capture system documented two expert pianists performing a B major scale, hands together, at 4, 6, 8, 9, and 10 notes/sec. Biomechanical modeling quantified WIL. Changes in motor behaviour were observed at 8 notes/sec. Individualized anthropometry influenced the range of motor strategies available to each pianist. The pianist with the larger hand span employed a flexion/extension wrist strategy as a compensatory means for effort reduction, while the pianist with the smaller hand span employed a radial/ ulnar deviation strategy. The current study provides a new perspective in addressing PRMP among pianists by rationalizing anthropometric potentials in terms of ergonomic parameters and documenting the availability and utility of effort-reduction strategies in the wrists during piano performance as performers consider PRMP risk and avoidance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".