Anthropometric analysis and development of a simulink model or classifying body shapes using fuzzy logic

Abstract

This research analyzes the body shapes of 353 adult males (aged 18–60) in Southern Vietnam. The research employed a combination of cross-sectional statistical methods and fuzzy logic to ensure accuracy. Factor analysis first extracted four principal body dimensions. Using these as a basis, K-means clustering and analysis of variance (ANOVA) identified six distinct body shape groups. The author applied WHO standards, including body mass index (BMI), difference between chest and waist measurements (DROP), and waist-to-hip ratio (WHR), to differentiate between types, ranging from thin to obese. The average height of Southern Vietnamese men has increased compared to the TCVN 5782:2009 standard. This reflects a positive development in physical growth. The author developed a fuzzy logic model for rapid classification. The model uses height and weight as inputs to manage measurement uncertainty effectively. It has high practical value for the apparel industry, especially in 3D avatar generation. This research contributes to the scientific understanding of anthropometric variation. It also provides a foundation for personalized fashion and body shape prediction in Industry 4.0.