A statistical model for developing body size charts for garments

Gupta, Deepti; Gangadhar, B.R.

doi:10.1108/09556220410555641

Cited by 83 publications

(58 citation statements)

References 5 publications

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“…Consequently, the aggregate loss was performed in this study by using the Euclidean distance divided by the number of individuals in each size group. The ideal value for aggregate loss of fit can be calculated using the number of body dimensions considered and allowing for a 2.54 cm (1 inch) deviation of the body dimension from the assigned size (Gupta & Gangadhar, 2004). In this study, the aggregate loss was calculated using two control dimensions (bust girth and waist girth).…”

Section: Development Of Body Measurement Standardsmentioning

confidence: 99%

“…In this study of 744 women, the mean height was 154 cm with a standard deviation of 5 cm. The population was divided into three height categories using classified rules developed by Indian experts (Gupta & Gangadhar, 2004). The three height categories were short (>149 cm), average (149-159 cm), and tall (>159 cm).…”

Section: Population Classification Based On Height Factormentioning

confidence: 99%

“…The results of the factor analysis listed in Table 2 demonstrate that the most important variables within the girth factor were bust girth and waist girth. Because these two measurements are also the most important anthropometric variables in clothing manufacturing (Gupta & Gangadhar, 2004;Winks, 1997), they were chosen to represent the girth factor. For height-related anthropometric variables, body height correlated with all variables, so it was selected to represent the height factor.…”

Section: Factor Analysismentioning

confidence: 99%

“…These approaches were dissimilar, and the figure types discerned from body measurement standards were classified according to different approaches. In related studies, Tryfos (1986) used an integer-programming approach to classify sizes and maximize expected sales; Chen (1998) focused on anthropometric measurements of women to develop body measurement standards; McCulloch, Paal, and Ashdown (1998) used a nonlinear optimization method to derive a set of sizes; Chang and Shen (1999) applied multivariate analysis to develop body measurement standards for young women; Laing, Holland, Wilson, and Niven (1999) used statistical analysis to develop body measurement standards for protective apparel used by firemen; Moon and Nam (2003) measured the anthropometric data of women to classify lower-trunk figure types; Gupta and Gangadhar (2004) used a statistical method to develop body measurement standards for young women; Hsu and Wang (2005) developed body measurement standards to determine the sizes of soldiers using a decision tree technique; and Hsu, Lin, and Wang (2006) used multivariate analysis to establish body measurement standards for women's apparel.…”

Section: Introductionmentioning

confidence: 99%

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Developing accurate industrial standards to facilitate production in apparel manufacturing based on anthropometric data

Hsu¹

2009

Hum Ftrs & Erg Mfg Svc

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Clothing and apparel are high value-added products, and body measurement standards are crucial industrial standards in apparel manufacturing. These standards enable manufacturers to predict sales of different sizes and set production quantities accordingly, resulting in accurate material control and production planning. Many standards use a range of approximate girth differences based on anthropometric data to determine figure types, but these figure types cannot be accurately classified. Moreover, little research has been done to apply the girth ratio approach to identify figure types. This study proposes an accurate girth ratio approach to identify figure types for middle-aged women; the approach can then be used to develop industrial standards. By applying these findings, figure types can be precisely classified. New body measurement standards can then be developed. These industrial standards can help manufacturers improve the fit of mass-produced apparel, and their application in manufacturing can reduce production costs and increase market competitiveness. C 2009 Wiley Periodicals, Inc.

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Section: Development Of Body Measurement Standardsmentioning

confidence: 99%

Section: Population Classification Based On Height Factormentioning

confidence: 99%

Section: Factor Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Developing accurate industrial standards to facilitate production in apparel manufacturing based on anthropometric data

Hsu¹

2009

Hum Ftrs & Erg Mfg Svc

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“…That is why, multivariate approaches have been proposed to develop sizing systems. Principal components are often used to reduce the dimension of our anthropometric data set, and the two first principal components are used to generate bivariate distributions [4,13,20,12,14,27]. As an alternative to bivariate distributions, clustering techniques using partitioning methods, like k-means algorithms, group the population into morphologies using the complete set of anthropometric variables as input [6,32,23]).…”

Section: Introductionmentioning

confidence: 99%

Apparel sizing using trimmed PAM and OWA operators

Ibáñez

Vinué

Alemany

et al. 2012

Expert Systems with Applications

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This paper is concerned with apparel sizing system design. One of the most important issues in the apparel development process is to define a sizing system that provides a good fit to the majority of the population. A sizing system classifies a specific population into homogeneous subgroups based on some key body dimensions. Standard sizing systems range linearly from very small to very large. However, anthropometric measures do not grow linearly with size, so they can not accommodate all body types. It is important to determine each class in the sizing system based on a real prototype that is as representative as possible of each class. In this paper we propose a methodology to develop an efficient apparel sizing system based on clustering techniques jointly with OWA operators. Our approach is a natural extension and improvement of the methodology proposed by McCulloch et al in 1998 [22],and we apply it to the anthropometric database obtained from a anthropometric survey of the Spanish female population, performed during 2006.

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Classification of upper lateral body shapes for the apparel industry

Choi

Nam

2010

Hum Ftrs & Erg Mfg Svc

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The lateral body shape is a critical determiner of the fit of garments. Either visual assessment or statistical analysis methods have been used to classify the lateral body types. These methods are limited to some extent since various anthropometric features inherently coexist and interact in a human body shape. This study aims to develop objective criteria for the classification of upper lateral body shapes integrating visual assessment and statistical analysis. The three-dimensional scan data of 246 women between 18 and 49 years old were visually classified into four lateral body shapes by an expert panel. In addition, the back space and lateral angles extracted from the scan data were employed for further statistical analyses. Multinomial logistic regressions were used to develop logit models for lateral body types. It was concluded that the resulting logit models could classify lateral body types and calculate the probability of a set of body scan data being classified as a certain lateral body type. It is expected that this probability might be a guideline to quantify the characteristics of the lateral body shape in the apparel industry. C

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A statistical model for developing body size charts for garments

Cited by 83 publications

References 5 publications

Developing accurate industrial standards to facilitate production in apparel manufacturing based on anthropometric data

Developing accurate industrial standards to facilitate production in apparel manufacturing based on anthropometric data

Apparel sizing using trimmed PAM and OWA operators

Classification of upper lateral body shapes for the apparel industry

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