Journal of Fiber Bioengineering & Informatics, 16 (2023), pp. 45-55.
Published online: 2023-10
Cited by
- BibTex
- RIS
- TXT
Abdominal protrusion is increasingly common among middle-aged and elderly women, and the current standard sizing system fails to properly their body shape change. To improve the classification of abdominal bulge morphology in middle-aged and elderly women and enhance the garment fit, this paper screened 133 samples with abdominal bulge among 165 Chinese women aged 50-59 years old based on 3D anthropometric techniques and obtained abdominal morphological dimensions. Five main morphology parameters affecting abdominal convexity were summarized, and the abdominal morphology was classified into four types for simulation. The abdominal regression models and girth fitting models were established and validated by combining the feature indexes related to pants. Results showed that each abdominal convexity type has obvious and specific clustering characteristics, and the regression models are valid and practical for personalized clothing development.
}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim02161}, url = {http://global-sci.org/intro/article_detail/jfbi/22059.html} }Abdominal protrusion is increasingly common among middle-aged and elderly women, and the current standard sizing system fails to properly their body shape change. To improve the classification of abdominal bulge morphology in middle-aged and elderly women and enhance the garment fit, this paper screened 133 samples with abdominal bulge among 165 Chinese women aged 50-59 years old based on 3D anthropometric techniques and obtained abdominal morphological dimensions. Five main morphology parameters affecting abdominal convexity were summarized, and the abdominal morphology was classified into four types for simulation. The abdominal regression models and girth fitting models were established and validated by combining the feature indexes related to pants. Results showed that each abdominal convexity type has obvious and specific clustering characteristics, and the regression models are valid and practical for personalized clothing development.