USING MULTIVARIATE ANALYSIS TO SELECT ACCOMMODATION BOUNDARY MANIKINS FROM A POPULATION DATABASE

Digital Human Models (DHMs) are a tool that can be used to aid in determining dimensions for human-centered designs. DHMs have the ability to represent the anthropometric extremes of the population and help to determine which dimensions should be used to acquire a certain level of accommodation within a population. It is not possible to use current techniques for selecting manikins that represent a population, like principal component analysis (PCA), the application of design families, or percentiles due to these methods having a lower output accommodation levels than expected. The purpose of this research is to provide a multivariate analysis based on Pareto optimization. This method determines a pool of manikins representing the total target population when comparing up to three anthropometric dimensions within a database. This pool will act as boundary manikins for a given level of accommodation.