Title: Generation and evaluation of distributed cases by clustering of diverse anthropometric data

Authors: Erik Brolin; Dan Högberg; Lars Hanson; Roland Örtengren

Addresses: School of Engineering Science, University of Skövde, Box 408, SE-541 28, Skövde, Sweden; Department of Product and Production Development, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden ' School of Engineering Science, University of Skövde, Box 408, SE-541 28, Skövde, Sweden ' Industrial Development, Scania, Scania CV, SE-151 87 Södertälje, Sweden; School of Engineering Science, University of Skövde, Box 408, SE-541 28, Skövde, Sweden; Department of Product and Production Development, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden ' Department of Product and Production Development, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden

Abstract: This paper describes a study where diversity in body size, strength and joint range of motion, together with diversity in other capability measurements, is included in the process of generating data for a group of test cases using cluster analysis. Descriptive statistics and correlation data was acquired for 15 variables for different age groups and both sexes. Based on this data, a population of 10,000 individuals was synthesised using correlated random numbers. The synthesised data was used in cluster analyses where three different clustering algorithms were applied and evaluated; hierarchical clustering, k-means clustering and Gaussian mixture distribution clustering. Results from the study show that the three clustering algorithms produce groups of test cases with different characteristics, where the hierarchical and k-means algorithm give the most diverse results and where the Gaussian mixture distribution gives results that are in between the first two.

Keywords: anthropometry; diversity; distributed cases; body size; body strength; flexibility; joint ROM; range of motion; capability measurements; digital human models; DHM; modelling; anthropometric data; cluster analysis; hierarchical clustering; k-means clustering; Gaussian mixture distribution clustering.

DOI: 10.1504/IJHFMS.2016.079706

International Journal of Human Factors Modelling and Simulation, 2016 Vol.5 No.3, pp.210 - 229

Received: 02 Sep 2015
Accepted: 02 Mar 2016

Published online: 10 Oct 2016 *

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