Validation of 'Female Figure Identification Technique (FFIT) for Apparel' Methodology

FFIT for apparel;Body shapes

Devarajan, Priya ; Dr. Cynthia Istook, Committee Chair,Dr. Mitzi Montoya-Weiss, Committee Member,Dr. Trevor Little, Committee Member,Devarajan, Priya ; Dr. Cynthia Istook ; Committee Chair ; Dr. Mitzi Montoya-Weiss ; Committee Member ; Dr. Trevor Little ; Committee Member

University:North Carolina State University

关键词: FFIT for apparel;    Body shapes;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/2577/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

The emergence of sophisticated 3-dimensional body measurement technologies and apparel CAD systems has allowed major apparel retailers to use made-to-measure as a viable marketing tool. To successfully implement mass customization strategies in the apparel industry, it is necessary to have a sophisticated body measurement technology, an integration tool that allows the body measurements to be analyzed and directed into CAD systems and a standard set of patterns that could be altered according to the customer's body shape determined from their body scan data. It is necessary to identify the standard body shapes representative of the current population upon which the standard patterns would be based. The software 'Female Figure Identification Technique (FFIT) for Apparel' (Simmons, 2002) developed at the College of Textiles of North Carolina State University has identified nine different shapes from a convenience sample of 253 female subjects. It is the only software currently available that uses three-dimensional data in acquiring the body shapes of females. It is now necessary to statistically verify whether the shapes identified by the software represent the shapes of the female population of the U.S. This study was aimed at validating the software 'Female Figure Identification Technique (FFIT) for Apparel' using multivariate statistical methods. Discriminant analysis and Multivariate Analysis of Variance (MANOVA) were used for this purpose. The findings of the study show that the FFIT for Apparel software was more accurate than the discriminant function in classifying the female figures into their correct shapes. The five body measurement ratios used in the software code were better in accurately predicting the body shapes than random chance. The nine body shapes identified by the software were statistically different and they could be used as a basis to construct the standard patterns in CAD systems thus simplifying the process of customizing the patterns for each individual. In addition, the shapes could be used in developing new sizing standards for the apparel industry.