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Research Papers

Including Preference in Anthropometry-Driven Models for Design

[+] Author and Article Information
Christopher J. Garneau

 Pennsylvania State University, University Park, PA 16802cjgarneau@psu.edu

Matthew B. Parkinson

Department of Mechanical Engineering, Engineering Design Program, Pennsylvania State University, University Park, PA 16802parkinson@psu.edu

J. Mech. Des 131(10), 101006 (Sep 16, 2009) (6 pages) doi:10.1115/1.3211092 History: Received August 26, 2008; Revised June 12, 2009; Published September 16, 2009

In the design of artifacts that interact with people, the spatial dimensions of the target user population are often used to determine the requirements of the engineered artifact. The expected variability in body dimensions (called “anthropometry”) is used to indicate how much adjustability or how many sizes are required to accommodate the intended user population. However, the quantification of anthropometric variability alone is not sufficient to make these kinds of assessments in many situations. For example, two vehicle drivers with similar body dimensions might have different preferred locations for the seat. In these situations, preference can be broken down into two components: that explained by body size and the variability that remains. By quantifying the magnitude of both sources, preference can be included in modeling strategies and design decision-making. This improves the accuracy of models and predictions, and can facilitate the application of design automation tools such as optimization and robust design methodologies, resulting in products that are safer, cost effective, and more accessible to broader populations (including people with disabilities). In contrast, failure to include variability in preference that is not attributable to anthropometry can produce misleading results that under- or over-approximate accommodation and prescribe inappropriate amounts of adjustability. A simulation-based approach for modeling both sources of variability and conducting designing for human variability (DfHV) assessments is presented. A stochastic component based on the residual variance in regression analysis relating body dimensions to experimental data is included in the predictive model. This ensures that a distribution of preferred configurations is produced for any given set of body dimensions. The effect of including both components of preference is quantified by comparing this approach to two traditional DfHV approaches in the context of a simple, univariate case study to determine the appropriate allocation of adjustability to achieve a desired accommodation level.

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Copyright © 2009 by American Society of Mechanical Engineers
Topics: Dimensions , Design
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Figures

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Figure 1

Body lengths expressed as a proportion of stature (17)

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Figure 2

Selected seat height plotted against stature for the 42-member sample, with regression line

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Figure 3

Distribution of 1000 random statures used in the hybrid-ResVar method

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Figure 4

Preferred seat height predicted using a regression model relating stature to experimental data. The predictors were 1000 randomly selected statures in the ANSUR database. The absence of the preference component unrelated to anthropometry compresses the data to a single line representing the average preferred configuration for a given body dimension (stature in this case).

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Figure 5

The stature and seat selections of the 1000-member sample are plotted with both components of preference included. Adjustment limits defined by the hybrid-ResVar method are denoted by the lines extending across the plot, and adjustable ranges defined by the manikin-k, manikin-ANSUR, hybrid-mean, and population model methods are denoted by the bars at the left.

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