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Generation and evaluation of distributed cases by clustering of diverse anthropometric data
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden. (Användarcentrerad produktdesign, User Centred Product Design)ORCID iD: 0000-0002-0125-0832
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Användarcentrerad produktdesign, User Centred Product Design)ORCID iD: 0000-0003-4596-3815
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Industrial Development, Scania, Scania CV, Södertälje, Sweden / Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden. (Användarcentrerad produktdesign, User Centred Product Design)ORCID iD: 0000-0002-7232-9353
Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden.
2016 (English)In: International Journal of Human Factors Modelling and Simulation, ISSN 1742-5557, Vol. 5, no 3, 210-229 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
InderScience Publishers, 2016. Vol. 5, no 3, 210-229 p.
Keyword [en]
anthropometry, diversity, distributed cases, clustering, strength, flexibility, joint range of motion, ROM, capability, digital human modelling, DHM
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-13095DOI: 10.1504/IJHFMS.2016.10000533OAI: oai:DiVA.org:his-13095DiVA: diva2:1046337
Projects
CROMM
Funder
VINNOVA, 2012-04584
Available from: 2016-11-14 Created: 2016-11-13 Last updated: 2017-01-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Language
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