Individualized generation of women's prototype based on the classification of body shape

• Published in: International journal of industrial ergonomics Vol. 103; p. 103631
• Main Authors: Jin, Shouning, Gu, Bingfei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024
• Subjects: Body shape classification; Pattern generation; Surface flattening; Women's prototype; Body shape classification; Women's prototype; Surface flattening; Pattern generation
• ISSN: 0169-8141
• DOI: 10.1016/j.ergon.2024.103631

The traditional prototype method only relies on the basic dimensions of the human body such as bust girth and back length to make the prototype pattern, ignoring the differences of human body details dimensions. In order to improve the fit of garments, the research on intelligent generation technology of individualized garment patterns has become one of the hot spots in the field of clothing. Based on the 3D body scanning technology, we proposed a method for generating prototype pattern based on body shape classification. The proposed method takes as inputs the body parameters (width, thickness and angle), while the output of the method is one of the four pattern generation rules-"Y" shape, "V" shape, "I" shape, or "X" shape. 207 subjects were divided into four categories based on the body angle parameters, namely "Y" shape, "V" shape, "I" shape and "X" shape, and a shape recognition model was built. The pattern database is required to obtain pattern generation rules by 3D point cloud reconstruction and flattening. Combined with the human body shape parameters, the mathematical formulas of the feature points on the pattern landmarks are built. The accuracy of body shape recognition model is 97.4%. The error analysis of the predicted pattern parameters shows that 90% of the pattern feature points have a goodness of fit above 0.7. In the 5 main landmarks, the proportion of pattern within the absolute error range is more than 80%, indicating that the prediction effect of the pattern is good. This method can be applied to the process of automatic pattern generation system based 2D measurement to improve work efficiency. •The accuracy of female body shape recognition model can reach 97.4%.•Prototype pattern generation rules based on body shape classification.•We convert 3D point cloud data into 2D pattern data.•Pattern feature points can be calculated by 13 human body parameters.•90% of the pattern feature points have a goodness of fit above 0.7.