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Record W4285740651 · doi:10.7717/peerj.13495

The effects of isometric hand grip force on wrist kinematics and forearm muscle activity during radial and ulnar wrist joint perturbations

2022· article· en· W4285740651 on OpenAlexafffund
Kailynn Mannella, Garrick N. Forman, Maddalena Mugnosso, Jacopo Zenzeri, Michael W.R. Holmes

Bibliographic record

VenuePeerJ · 2022
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsBrock University
FundersCanada Research Chairs
KeywordsWristIsometric exerciseForearmElectromyographyAnatomyKinematicsPhysical medicine and rehabilitationUlnar deviationAngular displacementUpper limbDisplacement (psychology)MedicineOrthodonticsPhysicsPhysical therapyPsychologyAcousticsClassical mechanics

Abstract

fetched live from OpenAlex

The purpose of this work was to investigate forearm muscle activity and wrist angular displacement during radial and ulnar wrist perturbations across various isometric hand grip demands. Surface electromyography (EMG) was recorded from eight muscles of the upper extremity. A robotic device delivered perturbations to the hand in the radial and ulnar directions across four pre-perturbation grip magnitudes. Angular displacement and time to peak displacement following perturbations were evaluated. Muscle activity was evaluated pre- and post-perturbation. Results showed an inverse relationship between grip force and angular displacement ( p ≤ 0.001). Time to peak displacement decreased as grip force increased ( p ≤ 0.001). There was an increase in muscle activity with higher grip forces across all muscles both pre-and post-perturbation ( p ≤ 0.001) and a greater average muscle activity in ulnar as compared to radial deviation ( p = 0.02). This work contributes to the wrist joint stiffness literature by relating wrist angular displacement to grip demands during novel radial/ulnar perturbations and provides insight into neuromuscular control 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.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.007
GPT teacher head0.192
Teacher spread0.185 · 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 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

Citations9
Published2022
Admission routes2
Has abstractyes

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