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Record W2289474317 · doi:10.1080/00140139.2015.1130861

Peak activation of lower limb musculature during high flexion kneeling and transitional movements

2016· article· en· W2289474317 on OpenAlexafffund
David C. Kingston, Liana M. Tennant, Helen Chong, Stacey M. Acker

Bibliographic record

VenueErgonomics · 2016
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKneelingLower limbPhysical medicine and rehabilitationHip flexionMedicinePhysical therapyRange of motionSurgery

Abstract

fetched live from OpenAlex

Few studies have measured lower limb muscle activation during high knee flexion or investigated the effects of occupational safety footwear. Therefore, our understanding of injury and disease mechanisms, such as knee osteoarthritis, is limited for these high-risk postures. Peak activation was assessed in eight bilateral lower limb muscles for twelve male participants, while shod or barefoot. Transitions between standing and kneeling had peak quadriceps and tibialis anterior (TA) activations above 50% MVC. Static kneeling and simulated tasks performed when kneeling had peak TA activity above 15% MVC but below 10% MVC for remaining muscles. In three cases, peak muscle activity was significantly higher (mean 8.9% MVC) when shod. However, net compressive knee joint forces may not be significantly increased when shod. EMG should be used as a modelling input when estimating joint contact forces for these postures, considering the activation levels in the hamstrings and quadriceps muscles during transitions. Practitioner Summary: Kneeling transitional movements are used in activities of daily living and work but are linked to increased knee osteoarthritis risk. We found peak EMG activity of some lower limb muscles to be over 70% MVC during transitions and minimal influence of wearing safety footwear.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.293

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 studies0.0000.000
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 head0.005
GPT teacher head0.177
Teacher spread0.172 · 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 designBench or experimental
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

Citations13
Published2016
Admission routes2
Has abstractyes

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