Volume 6, Issue 4
Lower Body Classification of Young Women for Pants Size Optimization

Wen Wu, Rong Zheng & Yunchao Zhang

Journal of Fiber Bioengineering & Informatics, 6 (2013), pp. 453-465.

Published online: 2013-06

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  • Abstract

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.

  • Keywords

Lower Body Shape Factor Analysis Hipline Abdomen-hip Differential

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@Article{JFBI-6-453, author = {}, title = {Lower Body Classification of Young Women for Pants Size Optimization}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2013}, volume = {6}, number = {4}, pages = {453--465}, abstract = {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.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi12201309}, url = {http://global-sci.org/intro/article_detail/jfbi/4855.html} }
TY - JOUR T1 - Lower Body Classification of Young Women for Pants Size Optimization JO - Journal of Fiber Bioengineering and Informatics VL - 4 SP - 453 EP - 465 PY - 2013 DA - 2013/06 SN - 6 DO - http://doi.org/10.3993/jfbi12201309 UR - https://global-sci.org/intro/article_detail/jfbi/4855.html KW - Lower Body Shape KW - Factor Analysis KW - Hipline KW - Abdomen-hip Differential AB - 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.
Wen Wu, Rong Zheng & Yunchao Zhang. (2019). Lower Body Classification of Young Women for Pants Size Optimization. Journal of Fiber Bioengineering and Informatics. 6 (4). 453-465. doi:10.3993/jfbi12201309
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