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Record W3099470139 · doi:10.22215/etd/2016-11267

Detection of Forearm Muscle Fatigue During Piano Playing Using Surface Electromyography (sEMG) Analysis

2016· dissertation· en· W3099470139 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsCarleton University
Fundersnot available
KeywordsForearmPianoElectromyographyMuscle fatiguePhysical medicine and rehabilitationWristPsychologyPhysical therapyMedicineAnatomyArt

Abstract

fetched live from OpenAlex

Musculoskeletal injuries of the forearm, wrist, and hand are a significant problem for pianists. Approximately 60% of piano players experience playing-related injuries at some point in their careers; yet, the cause of injury is not well understood. Muscle fatigue may be a contributing factor. The focus of this work was to determine if muscle fatigue is detectable in the forearm muscles used for piano playing, if there is a difference in the development of muscle fatigue between pianists and non-pianists, and if there is an effect on how a pianist plays when their muscles are fatigued. A piano exercise and fatiguing exercise protocol were established and 11 pianists and 6 non-pianists were tested. It was hypothesized that pianists would show less incidence of fatigue than the non-pianists due to adaptations in muscle structure and task performance strategies.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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