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Record W4405396586 · doi:10.1016/j.jsr.2024.12.003

A predictive equation for maximum acceptable efforts based on duty cycle in repetitive back-involved tasks

2024· article· en· W4405396586 on OpenAlexafffund
Niromand Jasimi Zindashti, Karla Beltran Martinez, Alireza Golabchi, Mahdi Tavakoli, Hossein Rouhani

Bibliographic record

VenueJournal of Safety Research · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of Alberta HospitalUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsPoison controlDuty cycleHuman factors and ergonomicsInjury preventionOccupational safety and healthDutyCycle of violenceSuicide preventionComputer scienceEngineeringSimulationMedical emergencyTransport engineeringMedicineVoltageElectrical engineering

Abstract

fetched live from OpenAlex

• Development of a predictive equation correlating MAE with DC for back-involved repetitive tasks. • The equation shows a strong negative relationship between DC and normalized MAE. • Statistical analysis indicates the equation's universality across different scenarios with minor effects of parameters. • Predictive equation provides an individual-specific approach to mitigate the risk of musculoskeletal disorders. Introduction: There are many different tasks involved within a workplace, and assessing the required efforts for performing them depends on factors such as the human’s body posture, task nature, and number of repetitions. This study aims to develop an equation for back-involved repetitive tasks that relates the maximum acceptable effort (MAE), the maximum acceptable efforts that an individual can sustain for a specific task and is expressed as a percentage of the maximum strength, to the duty cycle, the amount of time an individual is engaged in a task relative to the total time. The equation was derived based on psychophysical data collected from previous studies on lifting, lowering, and carrying tasks. The literature search identified studies reporting maximum acceptable loads (e.g., forces and toques) for back-involved tasks. Method: Data analysis was done by calculating duty cycles and for each task. Statistical tests were conducted to compare the results across different parameters, such as sex, task nature, lifting box size, box distance from the body, and population percentages. Results: The results showed a strong negative relationship between duty cycle and MAE. This relationship shows that by increasing the duty cycle, MAE should be decreased to be acceptable and prevent worker’s fatigue. The developed equation was compared to existing equations for upper-limb tasks and demonstrated a close resemblance. Additionally, statistical analysis indicated that the proposed equation eliminated the effects of various parameters. The proposed equation provides an individual-specific approach for estimating MAEs and can contribute to preventing workers’ fatigue and injury and reducing their associated costs.

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)0.008
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.390
Teacher spread0.338 · 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 designSimulation or modeling
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

Citations4
Published2024
Admission routes2
Has abstractyes

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