Abstract

The present study developed a novel multivariate representative humanoid (RH) generation method called the boundary zone method (BZM), which consists of (1) the formation of a boundary zone (BZ) for a designated accommodation percentage (κ), (2) the clustering of anthropometric cases in the BZ, and (3) the selection of representative cases from the clusters. By using 1988 U.S. Army anthropometric data for κ = 90% and 10 anthropometric dimensions, the BZM was compared to existing methods, including the square method (SM), the rectangular method (RM), and the circular method (CM) in terms of multivariate accommodation percentage (MAP), outlier percentage, and normalized outlier magnitude. The MAP analysis showed that only the BZM could form a group of RHs that precisely satisfied the designated κ, whereas the RM formed over-accommodating RH groups and both the SM and CM formed under-accommodating RH groups. The outlier analysis identified that only the BZM generated relevant RHs within the body size ranges of the target population.

Highlights

  • A small group of digital humanoids representing the target population is used in the ergonomics design and evaluation of products and workstations in a digital environment such as JACK® and RAMSIS®

  • The percentile representative humanoid (RH) generation method is commonly employed for its simplicity, it is often criticized in terms of multivariate accommodation

  • The proposed boundary zone overcome thethe limitations of the existing multivariate generation method (BZM) wasTo evaluated with existing multivariate methods (SM, rectangular method (RM), and circular method (CM)) methods, the present study proposes a novel multivariate method that generates

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Summary

Introduction

A small group of digital humanoids (manikins, human models, or cases) representing the target population is used in the ergonomics design and evaluation of products and workstations in a digital environment such as JACK® and RAMSIS®. The use of a small group of representative humanoids (RHs) enables designers to efficiently apply the body size characteristics of the target population to product design and evaluation [1,2]. RHs created by the percentile method can accommodate a designated percentage of the target population for each individual AD, but not for multiple ADs [6,7]. In designing the height and width of a bus door for 95% of the U.S population using 1988 U.S Army anthropometric data (Gordon et al, 1988), the percentile method uses a 95th percentile humanoid with a 95th percentile stature (183.8 cm) and a 95th percentile bideltoid breadth (41.8 cm). The height and width of the door would meet the designated κ if the stature and bideltoid breadth of the target population are considered independently, but become unsatisfactory

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