Neuromotor variability partially explains different endurance capacities of expert pianists
Bibliographic record
Abstract
During fatiguing piano tasks, muscle fatigue develops differently between expert pianists. Differences in neuromotor strategies employed could explain a slower rate of fatigue development. The objective was to compare muscle activation and kinematic variabilities between ShortDuration (i.e., pianists with less endurance) and LongDuration groups. Results from 49 pianists showed that EMG activation variability of most shoulder and upper limbs muscles was greater for the ShortDuration group with time during two piano fatiguing tasks, namely Digital and Chord tasks. Segment acceleration variability, assessed using inertial measurement units, was also greater with time for the ShortDuration group at the right arm during the Digital task, and at the thorax and head during the Chord task. Finally, thorax lateroflexion variability increased with time for the LongDuration group (but not the ShortDuration group) during the Digital task. During the Chord task, wrist flexion variability was higher for the LongDuration group compared to the ShortDuration group. These results showed a direct effect of time on the pianists' acceleration variability and EMG activation variability. In contrast, a protective effect of fatigue development could be attributed to kinematic variability. Results also suggest a higher risk of injury among pianists in the ShortDuration group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".