(untitled) (Journal of Fiber Bioengineering and Informatics, 2013)
Pants fit have always been a problem in China's pant market. To qualitatively improve how well pants fit consumers, we analyzed the lower body shapes of 179 young women from an anthropometric aspect. We first used a 3D measuring method to obtain 85 measurements related to lower body shape. Then, by applying principal component factor analysis method, we used 7 principal components to describe lower body shape. The first 2 factors, heavy-thin factor and abdomen-hip factor, had the highest cumulative contribution rate, 40.475%. Therefore, the hipline of the first principal component and the abdomen-hip differential of the second principal component were used as 2 key indexes to classify the lower body into 9 types. After using both the interior extrapolation method based on interval division and the k-means cluster method to further classify the lower body shape, we concluded that the former is more suitable. Therefore, we classified lower body shape into 9 types, the coverage of which reached 80.45% of the total samples. By taking both the degree of stoutness of the lower body and the difference of abdomen-hip shape into consideration, this classification can provide a theoretical basis for pants size optimization to improve pants fit in the waist, abdomen, and hip portions.