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Female Figure Identification Technique (FFIT) for apparel part II: Development of shape sorting software

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Sizing standards used in the United States that identify the body measurements used in the design and development of clothing were established from identified "best practices" of the apparel industry. However, the industry as a whole has not adopted a single system of clothing sizing. We know that manufacturers and retailers use their own sizing systems as a marketing tool, convinced that this is a differential advantage of their product for their market. Regardless of the sizing systems used, however, almost all are based on the myth that humans have mathematically proportional bodies and that they grow in proportional ways. In addition, the shapes and proportions of today's American population differ greatly from the shapes of the generations before. So a variety of issues impact our inability to 'fit' the American customer of today. These fit issues continue to be a growing concern. Mass customization methodologies appear to provide a "solution" by allowing customized fit of apparel. A significant underlying problem exists, however, when attempting to alter a garment for fit based on one standard shaped garment product. "Extreme" alterations seldom provide the desired fit in the final garment. This discovery has led us to understand that optimal customization can only occur if customization starts from the most correctly shaped garment for each customer's "figure type". Thus a system was developed to identify female figure types using 3-D body scan data. This article, as Part Two of two, describes the process involved in the development of an expert shape sorting system using 3D body scan data. This software will enable the identification of personal body shapes, allowing the use of the most correctly shaped garment for the customization procedure that will better ensure satisfactory fit of the final garment.
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... For this study the researchers partnered with a leading apparel company to analyze 3D plus size body scans using the Female Figure Identification Technique (FFIT) for apparel [2,3]. However, during the course of the research it was discovered there were opportunities to modify the FFIT formulas to better include plus size 3D scans. ...
... In 2004, Simmons, Istook, and Devarajan developed a shape sorting software, called the Female Figure Identification Technique (FFIT) for apparel, to classify 3D body scans and identify body shapes [2,3]. The software uses anthropometric measurements captured from 3D body scans to sort subjects into one of nine shape categories: Hourglass, Top Hourglass, Bottom Hourglass, Spoon, Rectangle, Diamond, Oval, Triangle, and Inverted Triangle. ...
... For subjects #1 and #2 in Table 8, the negative difference between hip and bust measurements (hipbust) indicated that the abdomens of the scans were larger than hips. The formulas have an underlying assumption that the waist is always smaller than the bust and hip, as was the case with most scans under size 14 analyzed in previous studies [2,3,7]. In order to correct for the assumption that the waist was smaller than the bust or hip, a check for negative numbers was added to the Triangle, Rectangle, and Inverted Triangle calculations. ...
... Among the factors contributing to a good fit, body shape has increasingly been recognized as fundamental. Studies on body shape analysis have actively been conducted, but they were mainly examining the whole body Simmons, Istook, and Devarajan 2004), lower body (Song and Ashdown 2011), and breast (Pei, Park, and Ashdown 2019). Upper body shape components such as back curvature, shoulder forward/backward inclination, shoulder slope, scapular protrusion, and torso inclination greatly affect clothing fit, but there are few studies related to upper body shape analysis (Ashdown and Na 2008;Chen 2011;Choi and Nam 2010;Kim 2018). ...
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