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Record W2133489339 · doi:10.1682/jrrd.2011.03.0047

Effect of velocity on shoulder muscle recruitment patterns during wheelchair propulsion in nondisabled individuals: Pilot study

2012· article· en· W2133489339 on OpenAlexaff
Liping Qi, James M. Wakeling, Simon Grange, Martin Ferguson-Pell

Bibliographic record

VenueThe Journal of Rehabilitation Research and Development · 2012
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectromyographyWheelchairPropulsionManual wheelchairPhysical medicine and rehabilitationMuscle fatigueLeg muscleSimulationMedicinePhysicsComputer science

Abstract

fetched live from OpenAlex

This study investigated how the recruitment patterns of shoulder muscles were influenced by wheelchair propulsion speed. Electromyography (EMG) activity of seven muscles was recorded with surface electrodes on 15 nondisabled subjects during wheelchair propulsion on a stationary ergometer. Kinetic data were measured by a SmartWheel. EMG total intensities were calculated to describe the muscle activation level, while the angles formed by first principal component and second principal component loading scores were calculated to describe the spectral content of EMG signals. Significant differences were observed in kinetic variables between the two testing speeds (p < 0.05). The EMG intensity of the tested muscles increased significantly with increased speed (p < 0.05). The push muscles showed a longer EMG duration, whereas the recovery muscles exhibited significantly earlier EMG onset and peak activities at the fast speed. The smaller angle values in the fast propulsion speed indicated that faster motor units were recruited to match the mechanical requirement for the faster motion. Pushing a manual wheelchair at a faster speed not only requires a higher level of propulsive muscle activity but also a higher level of recovery muscle activation.

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)0.019
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.161
GPT teacher head0.462
Teacher spread0.301 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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