Classification of Upper Body Fits Using Fit Models

The fit model defines the body measurements a garment retail company has determined and expresses the proportional relationships between body parts that are basic to achieving the company fit. Fit refers to how clothing conforms or differs on a person’s body. A company’s fit is an essential factor that sets its products apart from those of other companies. This implies that, usually, garments of the same size but from a different brand have different fits. This chapter applies a fuzzy set and linear regression-based classification method to find the upper body fits of a simulated target population for developing women’s swimwear design for one of Mexico’s largest apparel retailers. Six upper body parts were classified from very loose to very tight, including the neutral level concerning size M. In this work, we set up a supervised learning model to classify body measurements according to expert perceptions of women’s upper body fits. We define a classification function for each body position. Considering the vagueness and imprecision of expert perceptions, we classify data of each classification function into five levels using fuzzy techniques. The results will help develop a body-sizing system for garment design adapted to the Mexican population.