A method to improve the accuracy of pair-wise combinations of anthropometric elements when only limited data are...

Designers and ergonomists occasionally must produce anthropometric models of workstations with only summary percentile data available regarding the intended users. Until now the only option available was adding or subtracting percentiles of the anthropometric elements, e.g. heights and widths, used in the model, despite the known resultant errors in the estimate of the percent of users accommodated. This paper introduces a new method, the Median Correlation Method (MCM) that reduces the error.

Compare the relative accuracy of MCM to combining percentiles for anthropometric models comprised of all possible pairs of five anthropometric elements. Describe the mathematical basis of the greater accuracy of MCM.

MCM is described. 95th percentile accommodation percentiles are calculated for the sums and differences of all combinations of five anthropometric elements by combining percentiles and using MCM. The resulting estimates are compared with empirical values of the 95th percentiles, and the relative errors are reported.

The MCM method is shown to be significantly more accurate than adding percentiles. MCM is demonstrated to have a mathematical advantage estimating accommodation relative to adding or subtracting percentiles.

The MCM method should be used in preference to adding or subtracting percentiles when limited data prevent more sophisticated anthropometric models.