3D 인체측정 데이터를 활용한 성인 여성의 하반신 몸통 체형 유형화

Abstract

This study aimed to identify the structural characteristics and age-specific distribution patterns of adult women's lower torso body shapes through a multidimensional analysis of 3D body scan data. Factor analysis revealed four key components: horizontal dimensions, vertical dimensions, length dimensions, and thigh size, which together accounted for 87.30% of the total variance. K-means cluster analysis categorized the lower torso body shapes into four types: Long and Large Lower Body, Tall Lower Body with Average Thighs, Short Lower Body with Upper Bulk, and Short Lower Body with Prominent Thighs. The distribution of these types varied significantly across age groups. Specifically, the proportion of the Short Lower Body with Upper Bulk type increased with age, while younger age groups showed a higher prevalence of the Tall and Lower-Heavy types. These findings provide practical design guidelines for lower garment pattern making and customized clothing development based on body type and age. Additionally, the results suggest potential applications in the digital fashion industry, including 3D virtual fitting systems, AI-based recommendation platforms, and body type-specific sizing systems. This study offers foundational data for future research and policy development related to functional lower garments and inclusive body modeling standards.