Campus Units

Industrial and Manufacturing Systems Engineering

Document Type


Publication Version

Accepted Manuscript

Publication Date


Journal or Book Title

Theoretical Issues in Ergonomics Science

Research Focus Area(s)

​Operations Research, Ergonomics and Human Factors




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.


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.

Copyright Owner

Taylor & Francis Group, LLC



File Format


Available for download on Saturday, October 23, 2021

Published Version