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Record W4386021264 · doi:10.1016/j.apergo.2023.104111

Effects of a passive shoulder exoskeleton on muscle activity among Danish slaughterhouse workers

2023· article· en· W4386021264 on OpenAlexaff
Annett Dalbøge, Jeppe Frost, Regine Grytnes, Jean‐Sébastien Roy, Afshin Samani, David Høyrup Christiansen

Bibliographic record

VenueApplied Ergonomics · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsDanishExoskeletonMedicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

AIM: To evaluate the effect of a shoulder exoskeleton on muscle activity and to compare the effect with a lifting glove among slaughterhouse workers in occupational settings. MATERIALS AND METHODS: We conducted a crossover study of 26 workers measured during two work days with and without the use of a passive shoulder exoskeleton and a lifting glove at a Danish slaughterhouse. Electromyography sensors were placed bilateral on 5 shoulder muscles. The 10th, 50th, and 90th percentiles of muscle activity normalized by maximal voluntary contractions were measured and analyzed using mixed effect models. RESULTS: For the 50th percentiles of the agonist muscles, the exoskeleton reduced muscle activity bilaterally for deltoid anterior with up to 29.47%, deltoid middle with 10.22%, and upper trapezius with 22.21%. The lifting glove only reduced muscle activity for right deltoid anterior (36.59%) and upper trapezius (7.11%), but generally increased left muscle activity with up to 15.58%. DISCUSSION: The exoskeleton showed larger reductions in muscle activity compared to the lifting glove.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.704

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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