Title: Adaptive regression model for prediction of 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 presents and evaluates an adaptive linear regression model for the prediction of unknown anthropometric data based on a flexible set of known predictive data. The method is based on conditional regression and includes use of principal component analysis to reduce effects of multicollinearity between the predictive variables. Results from the study show that the proposed adaptive regression model produces more accurate predictions compared to a flat regression model based on stature and weight, and also compared to a hierarchical regression model, that uses geometric and statistical relationships between body measurements to create specific linear regression equations in a hierarchical structure. An additional evaluation shows that the accuracy of the adaptive regression model increases logarithmically with the sample size. Apart from the sample size, the accuracy of the regression model is affected by the number of, and on which measurements that are, variables in the predictive dataset.

Keywords: anthropometry; multivariate; regression; conditional; PCA; capability; digital human modelling; DHM.

DOI: 10.1504/IJHFMS.2017.10008080

International Journal of Human Factors Modelling and Simulation, 2017 Vol.5 No.4, pp.285 - 305

Received: 04 Jan 2016
Accepted: 02 Mar 2016

Published online: 28 Sep 2017 *

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