A common objective in designing for human variability is to consider the variability in body size and shape of the target user population. Since anthropometric data specific to the user population of interest are seldom available, the variability is approximated. This is done in a number of ways, including the use of data from populations that are well-documented (e.g., the military), proportionality constants, and digital human models. These approaches have specific limitations, including a failure to consider the effects of lifestyle and demography, resulting in products, tasks, and environments that are inappropriately sized for the actual user population, causing problems with safety, fit, and performance. This paper explores a regression-based approach in a context where the demographic distributions of descriptors (e.g., race/ethnicity, age, and fitness) are dissimilar for the database and target population. Also examined is a stratified regression model involving the development of independent anthropometry-estimation models for each racial group. When using regression with residual variance, stratification on the predictor demographics to obtain estimates of gender, stature, and BMI distributions is shown to be sufficiently robust for usual database-target population combinations. Consideration of demographic variables in development of the regression model provides marginal improvement, but could be appropriate in specific situations.

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