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Record W2159464815 · doi:10.1123/jab.2012-0221

The Spatial Dependency of Shoulder Muscle Demands for Seated Lateral Hand Force Exertions

2014· article· en· W2159464815 on OpenAlexaff
Alison C. McDonald, Elora C. Brenneman, Alan C. Cudlip, Clark R. Dickerson

Bibliographic record

VenueJournal of Applied Biomechanics · 2014
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDependency (UML)Physical medicine and rehabilitationMedicineGeologyComputer scienceOrthodonticsArtificial intelligence

Abstract

fetched live from OpenAlex

As the modern workplace is dominated by submaximal repetitive tasks, knowledge of the effect of task location is important to ensure workers are unexposed to potentially injurious demands imposed by repetitive work in awkward or sustained postures. The purpose of this investigation was to develop a three-dimensional spatial map of the muscle activity for the right upper extremity during laterally directed submaximal force exertions. Electromyographic (EMG) activity was recorded from fourteen muscles surrounding the shoulder complex as the participants exerted 40N of force in two directions (leftward, rightward) at 70 defined locations. Hand position in both push directions strongly influenced total and certain individual muscle demands as identified by repeated measures analysis of variance (P < .001). During rightward exertions individual muscle activation varied from 1 to 21% MVE and during leftward exertions it varied from 1 to 27% MVE with hand location. Continuous prediction equations for muscular demands based on three-dimensional spatial parameters were created with explained variance ranging from 25 to 73%. The study provides novel information for evaluating existing and proactive workplace designs, and may help identify preferred geometric placements of lateral exertions in occupational settings to lower muscular demands, potentially mitigating fatigue and associated musculoskeletal risks.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.011
GPT teacher head0.272
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations18
Published2014
Admission routes1
Has abstractyes

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