Abstract
Accurate simulation of garment–body interactions is essential for enhancing virtual prototyping in apparel design. However, conventional virtual prototyping platforms rely on rigid avatars that inadequately reflect the complex deformation of human soft tissues, particularly for tight-fitting sportswear. Biomechanical models have been developed to address this issue. Nevertheless, their subject-specificity and reliance on specific imaging data limited their applicability across diverse body types and motions. To overcome these limitations, this study establishes a comprehensive scientific basis for understanding the biomechanical characteristics of soft tissues in young female adults, based on population-level 4D body scanning analysis. Specifically, 4D body scanning technology was employed to quantitatively analyse lower body soft tissue deformation in 48 females wearing five various prototypes of sports leggings in both static and dynamic scenarios. Statistical models, including fundamental statistical analysis, analysis of variance (ANOVA), and clustering, were used to comprehend how body composition, legging design parameters, and movement scenarios affect garment–body interactions and identify significant effects and variations among diverse subjects and garment conditions. The findings provide essential insights for enhancing the generalization of biomechanical models integrated in virtual prototyping platforms.
Keywords
Design generalization; Virtual prototyping; Garment–body interaction; Parametric human modelling
DOI
https://doi.org/10.21606/iasdr.2025.855
Citation
Li, X., Ye, C.,and Yip, J.(2025) Establishing Generalizable Garment–Body Interaction Models through 4D Biomechanical Analysis, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.855
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 3 - Design, Art & Technology
Establishing Generalizable Garment–Body Interaction Models through 4D Biomechanical Analysis
Accurate simulation of garment–body interactions is essential for enhancing virtual prototyping in apparel design. However, conventional virtual prototyping platforms rely on rigid avatars that inadequately reflect the complex deformation of human soft tissues, particularly for tight-fitting sportswear. Biomechanical models have been developed to address this issue. Nevertheless, their subject-specificity and reliance on specific imaging data limited their applicability across diverse body types and motions. To overcome these limitations, this study establishes a comprehensive scientific basis for understanding the biomechanical characteristics of soft tissues in young female adults, based on population-level 4D body scanning analysis. Specifically, 4D body scanning technology was employed to quantitatively analyse lower body soft tissue deformation in 48 females wearing five various prototypes of sports leggings in both static and dynamic scenarios. Statistical models, including fundamental statistical analysis, analysis of variance (ANOVA), and clustering, were used to comprehend how body composition, legging design parameters, and movement scenarios affect garment–body interactions and identify significant effects and variations among diverse subjects and garment conditions. The findings provide essential insights for enhancing the generalization of biomechanical models integrated in virtual prototyping platforms.