Postural Variability in Piano Performance
Bibliographic record
Abstract
Variability is inevitable in human movement and posture, including piano performance, although little research has been conducted in this area. The purpose of this study was to determine if, when comparing individuals to themselves, pianists demonstrate consistent postural angles within a task across multiple measurements and to ascertain if, between various tasks, there are discernible task-related postural patterns. Fifteen pianists participated in this study. Each pianist returned for a total of three measurement sessions. The tasks they were required to perform at each session were quiet sitting, raising their hands on and off the keyboard, playing an ascending and descending scale, sight reading, and playing a piece in three expressive conditions (i.e., deadpan, projected, exaggerated). The following postural angles were calculated based on motion capture data collected during the performance of these tasks: craniovertebral angle, head tilt, head-neck-trunk angle, trunk angle, thoracic angle, thoracolumbar angle, and lumbar angle. The within-person variability ratio across the three measurements was calculated for each angle and across all tasks. Task-related patterns in angles were examined by comparing the same postural angle across different tasks. Results showed that there is a considerable amount of within-person variability, but not enough to be inconsistent over time. Task-related patterns indicate that reading a musical score or playing at the extreme ends of the keyboard tend to involve leaning closer to the instrument. Implications for future studies, intervention studies in particular, include taking more than a single baseline measurement to provide a more accurate picture of an individual pianist's typical posture.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".