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Digital Human Models are used extensively in virtual manufacturing to evaluate hand clearance and reach. Spatial assessments of accommodation are typically conducted using digital human models representative of the manufacturing population. Unfortunately, these models are often based on anthropometry gathered from sources that are not representative of the actual target worker population. For example, the size and shape might be based on data from the U.S. military, which differs in .fitness, age, and race distributions from the typical automotive manufacturing population. Ford ergonomists traced errors in accommodation predictions to these inaccurate representations. Using a recently developed statistical methodology incorporating principal components analysis, the anthropometry of the target worker population was synthesized. Using these new data, Ford updated the anthropometry of their digital human models to reflect changes due to secular trends in the U.S.

This study addressed the effect of scaling subjects in a virtual reality (VR) environment when performing ergonomic evaluations for assembly automotive tasks. Subjects were selected to fit into one of 4 anthropometric groups, ranging in size from a 5th percentile female to a 95th percentile male. Each subject performed 3 tasks while interacting with a digital rendering of a vehicle. Subjects were represented as a human manikin (Classic Jack 4.0, UGS) whose actions were driven by motion tracking (EvaRT, MotionAnalysis). Each subject performed the tasks under 4 different conditions; once with unscaled anthropometry and in three conditions where they were made to appear either larger or smaller than their actual height. Peak and cumulative low back loads and shoulder moments, as well as joint angles, were calculated and compared (ANOVA). In some cases subject scaled to a particular size performed differently than those that were actually that size.