This research project investigates foot morphology and size classification of young women in Zhejiang province through large-scale 3D foot scanning and data-driven analysis.

Using handheld structured-light 3D scanning, foot data from 100 participants aged 18–25 were collected and analyzed across 48 measurement parameters. Statistical methods including principal component analysis and double K-means clustering were applied to identify key morphological factors and classify female foot types across length, width, and arch height dimensions.

The study establishes a nine-category foot type system and evaluates its relationship to existing national shoe sizing standards. Findings provide practical implications for footwear design, size system optimization, and digital foot modeling, contributing to improved fit, comfort, and functional performance in women’s footwear.

Awarded Excellent Graduation Thesis

Related publication in Journal of Silk:
Foot type classification of young females based on double K-means cluster analysis