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This paper advances a method of extrapolating, to target populations, inter-relationships in the anthropometry of an existing population database. Previous methods, including those that are based on using proportionality constants and those that involve developing regression relations, make use of stature as the primary predictor. This new approach distinguishes itself by accounting for the variability, across all the measures, that is not correlated with stature or other anthropometry. It builds on previous efforts by incorporating both stature and body mass index (BMI) as basic predictors in a single-step regression analysis of existing anthropometric data. The method is validated and shown to produce anthropometric measures for a population that are equivalent to the true measures. Additionally, this paper examines the effectiveness of multi-step regression in predicting anthropometry.

Standing reach envelopes are important tools for the design of industrial and vehicle environments. Previous work in this area has focussed on manikin-based (where a few manikins are used to simulate individuals reaching within the region of interest) and population-based (where data are gathered on many individuals reaching in a constrained environment) approaches. Each of these methods has merits and shortfalls. The current work bridges the manikin- and population-based approaches to assessing reach by creating population models using kinematic simulation techniques driven by anthropometric data. The approach takes into account body dimensions, balance, and postural cost to create continuous models that can be used to assess designs with respect to both maximal and submaximal reaches. Cost is quantified as the degree to which the torso is involved in the reach, since the inclination of the torso is a good measure of lower-back load and may be related to subjective reach difficulty.