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Record W2168804768 · doi:10.1682/jrrd.2012.06.0109

Coordination patterns of shoulder muscles during level-ground and incline wheelchair propulsion

2013· article· en· W2168804768 on OpenAlexaff
Liping Qi, James M. Wakeling, Simon Grange, Martin Ferguson-Pell

Bibliographic record

VenueThe Journal of Rehabilitation Research and Development · 2013
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWheelchairElectromyographyPropulsionPhysical medicine and rehabilitationManual wheelchairGround reaction forceWork (physics)Cycle ergometerSimulationKinematicsEngineeringComputer scienceMedicinePhysicsAerospace engineeringHeart rateMechanical engineering

Abstract

fetched live from OpenAlex

The aim of this study was to investigate how the coordination patterns of shoulder muscles change with level-ground and incline wheelchair propulsion. Wheelchair kinetics and electromyography (EMG) activity of seven muscles were recorded with surface electrodes for 15 nondisabled subjects during wheelchair propulsion on a stationary ergometer and wooden ramp (4 degree slope). Kinetic data were measured by a SmartWheel. The kinetics variables and the onset, cessation, and duration of EMG activity from seven muscles were compared with paired t-tests for two sessions. Muscle coordination patterns across seven muscles were analyzed by principal component analysis. Push forces on the push rim and the percentage of push phase in the cycle increased significantly during incline propulsion. Propulsion condition and posture affected muscle coordination patterns. During incline propulsion, there was more intense and longer EMG activity of push muscles in the push phase and less EMG activity of the recovery muscles, which corresponded with the increased kinetic data total force output and longer push phase in the incline condition. This work establishes a framework for developing a performance feedback system for wheelchair users to better coordinate their muscle patterning activity.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.339
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.095
GPT teacher head0.403
Teacher spread0.307 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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