Campus Units
Industrial and Manufacturing Systems Engineering
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
10-23-2020
Journal or Book Title
Theoretical Issues in Ergonomics Science
Research Focus Area(s)
Operations Research, Ergonomics and Human Factors
DOI
10.1080/1463922X.2020.1836285
Abstract
As we seek to develop high fidelity human simulation models for ergonomic applications, the characterisation of the variability in human performance is needed. This technical note describes a method for generating probability density functions (PDFs) for one performance characteristic: trunk kinematics. A PDF from the Johnson family of distributions is defined by four parameters (γ, ξ, δ and λ) and can represent a variety of distributions. In this study, previously published trunk kinematic data were fit to Johnson distributions and regression equations for each of the four parameters were created as a function of starting lift height. Using regression coefficients and Monte Carlo simulation, PDFs for novel lifting conditions were generated. These predicted PDFs were compared with histograms of empirical data collected from a new group of ten lifters performing lifts in these novel conditions. A Kolmogorov–Smirnov goodness of fit test was performed to assess the quality of the fit. Seven of the predicted distributions of these kinematic variables were found to be a good fit with the novel empirical data.
Copyright Owner
Taylor & Francis Group, LLC
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Koenig, Jordyn; Norasi, Hamid; and Mirka, Gary, "Technical note: Using Johnson distributions to model trunk kinematics" (2020). Industrial and Manufacturing Systems Engineering Publications. 249.
https://lib.dr.iastate.edu/imse_pubs/249
Comments
This is an Accepted Manuscript of an article published by Taylor & Francis in Theoretical Issues in Ergonomics Science (2020), available online at DOI: 10.1080/1463922X.2020.1836285. Posted with permission.