Cluster Size Intelligence Prediction System for Young Women’s Clothing Using 3D Body Scan Data

Zhengtang Tan, Shuang Lin () and Zebin Wang
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Zhengtang Tan: College of Engineering and Design, Hunan Normal University, Changsha 410081, China
Shuang Lin: School of Art & Design, Taizhou University, Taizhou 318000, China
Zebin Wang: College of Engineering and Design, Hunan Normal University, Changsha 410081, China

Mathematics, 2024, vol. 12, issue 3, 1-19

Abstract: This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer’s body, thus enhancing both the fit and aesthetic value of the clothing.

Keywords: 3D body measurement; clothing fit design; cluster analysis; intelligence prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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