Feature Extraction and Expertise Analysis of Pianists' Motion-Captured Finger Gestures
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the analysis of expert piano playing gestures. It aims to extract quantitative and objective features to represent pianists’ hands gestures, and more specifically to enable characterization of the expertise level of pianists. To do so, four pianists with different expertise levels were recorded with a marker-based optical motion capture system while playing six different piano pieces. Movements were decomposed with principal component analysis, leading to uncorrelated subparts called eigenmovements. We observed that four eigenmovements allowed representation of the original movement with 80% accuracy, and less than ten eigenmovements were sufficient to represent it with 95% accuracy. The eigenvalues, representing the contribution of each eigenmovement in the original movement, allowed comparison of pianists with each other, and showed that more trained pianists seemed to use more eigenmovements, reflecting a better motor control of their hands.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it