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Adaptive regression model for synthesizing anthropometric population data
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Chalmers University of Technology, Gothenburg, Sweden. (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. (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. Chalmers University of Technology, Gothenburg, Sweden / Industrial Development, Scania CV, Södertälje, Sweden. (User Centred Product Design)ORCID iD: 0000-0002-7232-9353
Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden.
2017 (English)In: International Journal of Industrial Ergonomics, ISSN 0169-8141, E-ISSN 1872-8219, Vol. 59, 46-53 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents the development of an adaptive linear regression model for synthesizing of missing anthropometric population data based on a flexible set of known predictive data. The method is based on a conditional regression model and includes use of principal component analysis, to reduce effects of multicollinearity between selected predictive measurements, and incorporation of a stochastic component, using the partial correlation coefficients between predicted measurements. In addition, skewness of the distributions of the dependent variables is considered when incorporating the stochastic components. Results from the study show that the proposed regression models for synthesizing population data give valid results with small errors of the compared percentile values. However, higher accuracy was not achieved when the number of measurements used as independent variables was increased compared to using only stature and weight as independent variables. This indicates problems with multicollinearity that principal component regression were not able to overcome. Descriptive statistics such as mean and standard deviation values together with correlation coefficients is sufficient to perform the conditional regression procedure. However, to incorporate a stochastic component when using principal component regression requires raw data on an individual level.

Relevance to industry

When developing products, workplaces or systems, it is of great importance to consider the anthropometric diversity of the intended users. The proposed regression model offers a procedure that gives valid results, maintains the correlation between the measurements that are predicted and is adaptable regarding which, and number of, predictive measurements that are selected.

Place, publisher, year, edition, pages
2017. Vol. 59, 46-53 p.
Keyword [en]
Anthropometry, Regression, Multivariate, Conditional, PCA, Variance
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
User Centred Product Design
Identifiers
URN: urn:nbn:se:his:diva-13505DOI: 10.1016/j.ergon.2017.03.008ScopusID: 2-s2.0-85015082844OAI: oai:DiVA.org:his-13505DiVA: diva2:1089407
Funder
VINNOVA, 2012-04584
Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2017-05-24Bibliographically approved

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Brolin, ErikHögberg, DanHanson, Lars
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