An empirical description of the dispersion of 5th and 95th percentiles in worldwide anthropometric data applied to estimating accommodation with unknown correlation values
Affiliations: Faculty of Industrial Design Engineering, Delft University of Technology, Delft, The Netherlands
Correspondence:
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Corresponding author: Thomas J. Albin, High Plains Engineering Services 4425 34th Avenue South, Minneapolis, MN 55406, USA. Tel.: +1 612 724 6978; E-mail:T.J.Albin@tudelft.nl
Abstract:
BACKGROUND:
Anthropometric data are assumed to have a Gaussian (Normal) distribution,
but if non-Gaussian, accommodation estimates are affected. When data are
limited, users may choose to combine anthropometric elements by Combining
Percentiles (CP) (adding or subtracting), despite known adverse effects. OBJECTIVE:
This study examined whether global anthropometric data are Gaussian
distributed. It compared the Median Correlation Method (MCM) of combining
anthropometric elements with unknown correlations to CP to determine if MCM
provides better estimates of percentile values and accommodation. METHOD:
Percentile values of 604 male and female anthropometric data drawn from
seven countries worldwide were expressed as standard scores. The standard
scores were tested to determine if they were consistent with a Gaussian
distribution. Empirical multipliers for determining percentile values were developed. In a test case, five anthropometric elements descriptive of seating were
combined in addition and subtraction models. Percentile values were
estimated for each model by CP, MCM with Gaussian distributed data, or MCM
with empirically distributed data. RESULTS:
The 5th and 95th percentile values of a dataset of global
anthropometric data are shown to be asymmetrically distributed. MCM with
empirical multipliers gave more accurate estimates of 5th and 95th
percentiles values. CONCLUSIONS:
Anthropometric data are not Gaussian distributed. The MCM method is more
accurate than adding or subtracting percentiles.
