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Record W2082128399 · doi:10.1080/00140130701733301

Lower extremity preventive measures for slips – joint moments and myoelectric analysis

2008· article· en· W2082128399 on OpenAlexaff
Daniel Tik-Pui Fong, Youlian Hong, Jingxian Li

Bibliographic record

VenueErgonomics · 2008
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAnklePhysical medicine and rehabilitationCadenceKinematicsElectromyographyBicepsMedicineSwingGait analysisBiomechanicsSTRIDEMotion analysisOrthodonticsPhysical therapyAnatomyComputer scienceEngineering

Abstract

fetched live from OpenAlex

This study investigated the lower extremity preventive measures for slips on simulated slippery surfaces in construction worksites. A total of 15 harnessed Chinese males walked without slips on a 5-m walkway in 16 simulated conditions 10 times at their natural cadence. Joint moments were calculated from kinetics, kinematics and anthropometric data recorded from a force plate, a Novel Pedar system and a motion analysis system. Data were evaluated from footstrike to mid-stance at 10%-stance intervals. Electromyography signals from tibialis anterior, gastrocnemius, rectus femoris and biceps femoris in one stride were evaluated in four phases, including early/late stance and swing. Results showed that lower extremity preventive measures for slips included prolonged ankle plantar flexion moments from 25% to 92% stance, increased ankle plantar flexion moments from 30% stance to mid-stance, and diminished knee extension moments from 10% to 30% stance. Higher activity of rectus femoris and gastrocnemius were found in the late stance and the swing phase respectively.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.341

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 head0.022
GPT teacher head0.262
Teacher spread0.240 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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