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Record W2028933889 · doi:10.1016/j.proeng.2011.05.095

Development of an ergonomic musculoskeletal model to estimate muscle forces during vertical jumping

2011· article· en· W2028933889 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBicepsJumpingAnkleJumpAnatomyElectromyographyPhysical medicine and rehabilitationVertical jumpMedicineBiomedical engineeringMaterials scienceMathematicsGeologyPhysics

Abstract

fetched live from OpenAlex

This study investigated a musculoskeletal model that includes the function of the antagonistic muscles and biarticular muscles and models muscles acting across the hip, knee and ankle joints, simultaneously. Furthermore, this study can be applied to dynamic motions. One vertical jump trials were conducted to validate the proposed model. Electromyograms (EMGs) of tibials anterior, gastrocnemius, soleus, rectus femoris, vastus lateralis, semimembranosus, biceps femoris, short head and gluteus maximus were used to compare with the estimated muscle forces. The results showed that the muscle forces estimated by the proposed method had a stronger correlation with EMGs than those of an optimization method. The correlations of the proposed method and the optimization method were 0.4 and 0.01 of TA, 0.95 and 0.86 of GAS, 0.95 and 0.93 of SOL, 0.94 and 0.01 of RF, 0.93 and 0.97 of VAS, 0.83 and 0.91 of SM, 0.75 and 0.01 of BFSH and 0.95 and 0.92 of GMAX. Thus, the proposed method was considered to successfully estimate the muscle forces during vertical jumping.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.872

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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