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Record W4321444042 · doi:10.21203/rs.3.rs-2609671/v1

The Influence of Proximal Motor Strategies on Pianists’ Upper-Limb Movement Variability

2023· preprint· en· W4321444042 on OpenAlexafffund
Craig Turner, Étienne Goubault, Fabien Dal Maso, Mickaël Begon, Felipe Verdugo

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsTrunkUpper limbElbowKinematicsPhysical medicine and rehabilitationWristRange of motionShoulder jointMovement (music)Upper trunkMedicinePhysical therapyAnatomyPhysicsBiology

Abstract

fetched live from OpenAlex

Abstract Repetitive movements are considered a risk factor for developing practice-related musculoskeletal disorders. Intra-participant kinematic variability might help musicians reduce the risk of injury during repetitive tasks. No research has studied the effects of proximal motion (i.e., trunk and shoulder movement) on upper-limb movement variability in pianists. The first objective was to determine the effect of proximal movement strategies and performance tempo on both intra-participant joint angle variability of upper-limb joints and endpoint variability. The second objective was to compare joint angle variability between pianist’s upper-limb joints. As secondary objectives, we assessed the relationship between intra-participant joint angle variability and task range of motion (ROM) and documented inter-participant joint angle variability. The upper body kinematics of 9 expert pianists were recorded using an optoelectronic system. Participants continuously performed two right-hand chords (lateral leap motions) while changing movements based on trunk motion (with and without) and shoulder motion (counter-clockwise, back-and-forth, and clockwise) at two tempi (slow and fast). Trunk and shoulder movement strategies collectively influenced variability at the shoulder, elbow and, to a lesser extent, the wrist. Slow tempo led to greater variability at wrist and elbow flexion/extension compared to fast tempos. Endpoint variability was influenced only along the anteroposterior axis. When the trunk was static, the shoulder had the lowest joint angle variability. When trunk motion was used, elbow and shoulder variability increased, and became comparable to wrist variability. ROM was correlated with intra-participant joint angle variability, suggesting that increased task ROM might result in increased movement variability during practice. Inter-participant variability was approximately six times greater than intra-participant variability. Pianists should consider incorporating trunk motion and a variety of shoulder movements as performance strategies while performing leap motions at the piano, as they might reduce exposure to risks of injury.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.094
GPT teacher head0.438
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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