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Record W3130431070 · doi:10.1177/1071181320641227

Exercise Equipment Development for Safe Activation of the Medial Deltoid

2020· article· en· W3130431070 on OpenAlexaff
Selena Celic, Eun‐Sik Kim, Jill Urbanic

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2020
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDeltoid curveDeltoid muscleMedicinePhysical medicine and rehabilitationAnatomy

Abstract

fetched live from OpenAlex

The medial deltoid muscle is a small – and often neglected – muscle in comparison to other muscular systems in the immediately surrounding area of the upper body. If there is low or improper focus on the medial deltoid, there is a high risk of muscle imbalances and dysfunctions. Limited equipment exists today for proper training of the medial deltoid muscle; meaning there exists an opportunity to develop equipment which can safely and intuitively activate the medial deltoid muscle. To ensure maximization of the medial deltoid without increasing the activation of the surrounding muscles, the activation measurements of the anterior deltoid, posterior deltoid, and upper trapezius were also verified throughout the experiments with the seven exercises tested. This paper presents the pitfalls of current solutions, research of various studies and experiments, and confirms the activation levels and risks associated with various movement patterns.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.210
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicMuscle activation and electromyography studiesFrench-language works237,207