Generation and evaluation of distributed cases by clustering of diverse anthropometric data
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
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.
anthropometry, diversity, distributed cases, clustering, strength, flexibility, joint range of motion, ROM, capability, digital human modelling, DHM
Production Engineering, Human Work Science and Ergonomics
Research subject Technology
IdentifiersURN: urn:nbn:se:his:diva-13095DOI: 10.1504/IJHFMS.2016.10000533OAI: oai:DiVA.org:his-13095DiVA: diva2:1046337