New Math Fixes Hidden Bias in How Companies Design Products for Human Bodies
Researchers found that standard statistical methods systematically misrepresent human body measurements, skewing the digital models companies use to design everything from car seats to workstations. By applying simple mathematical transformations to real-world data, manufacturers can now create more accurate simulations that serve broader populations and reduce costly design failures.
Originaltitel: Skewed Boundary Confidence Ellipses for Anthropometric Data
<p>Some anthropometric measurements, such as body weight often show a positively skewed distribution. Different types of transformations can be applied when handling skewed data in order to make the data more normally distributed. This paper presents and visualises how square root, log normal and, multiplicative inverse transformations can affect the data when creating boundary confidence ellipses. The paper also shows the difference of created manikin families, i.e. groups of manikin cases, when using transformed distributions or not, for three populations with different skewness. The results from the study show that transforming skewed distributions when generating confidence ellipses and boundary cases is appropriate to more accurately consider this type of diversity and correctly describe the shape of the actual skewed distribution. Transforming the data to create accurate boundary confidence regions is thought to be advantageous, as this would create digital manikins with enhanced accuracy that would produce more realistic and accurate simulations and evaluations when using DHM tools for the design of products and workplaces.</p>