Female Body Shape Classifications and Their Significant Impact on Fabric Utilization

다국어 초록 (Multilingual Abstract)

In apparel manufacturing, more than 50 % cost is consumed by the textile fabric. Therefore companies have significant apprehensions in the fabric utilization. It can result in more efficient and cost-effective in fabric utilization if they are related to different body shapes. The purpose of this study is to classify female body shapes and evaluate fabric utilization efficiency for each category of the body shape. To this end, three dimensional (3D) body scans are collected from 124 young female subjects. For the body shape analysis, 3D body scans are processed by using Moore neighbor algorithm and region prop function to perceive the outermost shell. Moreover, both front and side view of the scans is processed for data reduction using Principle Component Analysis (PCA) and clustering using K-Means ++. It has been observed through our analysis of a dataset that female bodies can be categorized into four body shapes, that is, oval shape, circle shape, triangle shape, and rectangle shape. It has also been observed that all four body shape categories exhibit dissimilar anthropometric size measures. The result implies that these body shapes have devoured different fabric utilization for the garments (fitted trouser and fitted shirt). It has been noted that in fitted trouser and fitted shirt the most effective is the rectangle shape (cluster 4) and the least is the circle shape (cluster 2) in the fabric consumption. Similarly, the fitted trousers utilize less fabric while the fitted shirts consume more fabric in all body shapes. These findings provide a better reference of fabric utilization and cost-effectiveness to the apparel manufacturers while producing garments for different categories of the body shape.

참고문헌 (Reference)

1 W. K. Wong, 114 : 376-, 2008

2 Hutton, C. William, 74 : 73-, 2003

3 M. H. Sohn, 33 : 64-, 2015

4 P. Komarkova, 23 : 409-, 2013

5 Y. A. Nada, 105 : 15-, 2014

6 F. Xia, 38 : 86-, 2017

7 D. M. Rose, 34 : 3209-, 2007

8 Z. Li, 84 : 539-, 1995

9 T. Naveed, 88 : 1571-, 2017

10 E. Ko, 14 : 860-, 2013

11 E. K. Burke, 179 : 27-, 2007

12 D. A. Agbo, 1 : 29-, 2015

13 D. A. Agbo, 4 : 1-, 2013

14 V. Chattaraman, 31 : 291-, 2013

15 C. H. Hsu, 36 : 4185-, 2009

16 B. Allen, 22 : 587-, 2003

17 K. Okabe, 36 : 42-, 1995

18 R. Otieno, 11 : 366-, 2007

19 L. H. Bai, 27 : 113-, 2006

20 D. B. Nascimento, 128 : 379-, 2010

21 Y. L. Choi, 20 : 378-, 2010

22 X. Jing, 27 : 358-, 2015

23 A. Petrova, 26 : 227-, 2008

24 G. Mori, 28 : 1052-, 2006

25 J. Nantel, 18 : 73-, 2004

26 K. P. Simmons, NCSU 2003

27 K. Simmons, 4 : 1-, 2004

28 K. Simmons, 4 : 16-, 2004

29 V. Arzu, 23 : 46-, 2011

30 J. Yim Lee, 19 : 374-, 2007

31 W. Lee, 22 : 297-, 2010

32 S. Goel, 109 : 161-, 2015

33 L. Yin, 35 : 113-, 2014

34 M. J. Chung, 37 : 707-, 2007

35 I. Riter, "The Science of Personal Dress Complete Study:Body Shapes" Pleasanton 151-157, 2015

36 A. Knox, "Research Report for External Body" NTU 2015

37 Y. Zhong, "China Patent, ZL 200910194538.X"

38 R. E. Glock, "Apparel Manufacturing:Sewn Product Analysis" Prentice Hall 173-183, 2005

39 김남순, "An Effective Research Method to Predict Human Body Type Using an Artificial Neural Network and a Discriminant Analysis" 한국섬유공학회 19 (19): 1781-1789, 2018

40 "ASTM D5586/D5586M-10, Standard Tables of Body Measurements for Women Aged 55 and Older (All Figure Types)"