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Conditional Regression Model for Prediction of Anthropometric Variables
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society. Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden. (User Centred Product Design)
Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden / Industrial Development, Scania CV, Södertälje, Sweden. (Department of Product and Production Development)
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society. (User Centred Product Design)
Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden.
2013 (English)In: 2013 Digital human modeling symposium / [ed] Matt Reed, 2013Conference paper, (Refereed)
Abstract [en]

In digital human modelling (DHM) systems consideration of anthropometry is central. Important functionality in DHM tools is the regression model, i.e. the possibility to predict a complete set of measurements based on a number of defined independent anthropometric variables. The accuracy of a regression model is measured by how well the model predicts dependent variables based on independent variables, i.e. known key anthropometric measurements. In literature, existing regression models often use stature and/or body weight as independent variables in so-called flat regressions models which can produce estimations with large errors when there are low correlations between the independent and dependent variables. This paper suggests a conditional regression model that utilise all known measurements as independent variables when predicting each unknown dependent variable. The conditional regression model is compared to a flat regression model, using stature and weight as independent variables, and a hierarchical regression model that uses geometric and statistical relationships between body measurements to create specific linear regression equations in a hierarchical structure. The accuracy of the models is assessed by evaluating the coefficient of determination, R2 and the root-mean-square deviation (RMSD). The results from the study show that using a conditional regression model that makes use of all known variables to predict the values of unknown measurements is advantageous compared to the flat and hierarchical regression models. Both the conditional linear regression model and the hierarchical regression model have the advantage that when more measurements are included the models will give a better prediction of the unknown measurements compared to the flat regression model based on stature and weight. A conditional linear regression model has the additional advantage that any measurement can be used as independent variable. This gives the possibility to only include measurements that have a direct connection to the design dimensions being sought. Utilising the conditional regression model would create digital manikins with enhanced accuracy that would produce more realistic and accurate simulations and evaluations when using DHM tools for the design of products and workplaces.

Place, publisher, year, edition, pages
2013.
Keyword [en]
Anthropometry, Regression, Correlation, Multivariate, Prediction, Digital Human Modelling
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-8974OAI: oai:DiVA.org:his-8974DiVA: diva2:711557
Conference
2nd International Digital Human Modeling Symposium
Available from: 2014-04-10 Created: 2014-04-10 Last updated: 2014-05-23Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
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  • de-DE
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  • Other locale
More languages
Output format
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