Development of a new chinese bra sizing system based on breast anthropometric measurements

Zheng, Rong; Yu, Winnie; Fan, Jintu

doi:10.1016/j.ergon.2007.05.008

Cited by 124 publications

(87 citation statements)

References 18 publications

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“…Traditional methods are not useful because they use bivariate distributions to define a sizing chart and do not consider the variability of other relevant anthropometric dimensions. Recently, more sophisticated statistical methods have been de-veloped, especially using principal component analysis (PCA) and clustering (Gupta and Gangadhar 2004;Hsu 2009b;Luximon, Zhang, Luximon, and Xiao 2012;Hsu 2009a;Chung et al 2007;Zheng, Yu, and Fan 2007;Bagherzadeh, Latifi, and Faramarzi 2010). Peter Tryfos was the first to suggest an optimization method (Tryfos 1986).…”

Section: The Trimowa Methodologymentioning

confidence: 99%

Anthropometry: An R Package for Analysis of Anthropometric Data

Vinué¹

2017

J. Stat. Soft.

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The development of powerful new 3D scanning techniques has enabled the generation of large up-to-date anthropometric databases which provide highly valued data to improve the ergonomic design of products adapted to the user population. As a consequence, Ergonomics and Anthropometry are two increasingly quantitative fields, so advanced statistical methodologies and modern software tools are required to get the maximum benefit from anthropometric data. This paper presents a new R package, called Anthropometry, which is available on the Comprehensive R Archive Network. It brings together some statistical methodologies concerning clustering, statistical shape analysis, statistical archetypal analysis and the statistical concept of data depth, which have been especially developed to deal with anthropometric data. They are proposed with the aim of providing effective solutions to some common anthropometric problems, such as clothing design or workstation design (focusing on the particular case of aircraft cockpits). The utility of the package is shown by analyzing the anthropometric data obtained from a survey of the Spanish female population performed in 2006 and from the 1967 United States Air Force survey.This manuscript is also contained in Anthropometry as a vignette.

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Section: The Trimowa Methodologymentioning

confidence: 99%

Anthropometry: An R Package for Analysis of Anthropometric Data

Vinué¹

2017

J. Stat. Soft.

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“…Many studies have analyzed nude 3D scan data to determine breast shape and size Hong 2002, 2007;Chen et al, 2003;Kovacs et al, 2007;Zheng et al, 2007). Several methods have been used to measure breast size and then calculate bra cup size.…”

Section: Measuring Breast Sizementioning

confidence: 99%

“…3D body scan data have been analyzed to identify women's breast sizes and shapes (Kovacs et al, 2007;Wang et al, 2007). Previous studies established physical landmarks by directly contacting the subjects' skin to determine the boundary of the thorax and breast when conducting 3D body scans (Lee et al, 2004(Lee et al, , 2007Lee and Hong, 2006;Zheng et al, 2007;Kim et al, 2010). However, some subjects were uncomfortable with these types of measurements.…”

Section: J Ergon Soc Koreamentioning

confidence: 99%

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New Breast Measurement Technique and Bra Sizing System Based on 3D Body Scan Data

Oh¹,

Chun²

2014

Journal of the Ergonomics Society of Korea

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Objective: The aim of this study was to develop a method for measuring breast size from three-dimensional (3D) body scan image data.Background: Previous bra studies established reference points by directly contacting the subject's naked skin to determine the boundary of the breast. But some subjects were uncomfortable with these types of measurements. This study examined noncontact methods of extracting breast reference points from 3D body scan data that were collected while subjects were wearing standardized soft bras.Method: 3D body scan data of 32 Korean women were analyzed. The subjects were selected from the Size Korea 2010 study. The breast landmarks were identified by graphic analyses of slicing contour lines on 3D body scan data.Results: Three methods determining bra cup size were compared. The M1 and M2 methods determined cup size by calculating the difference between bust girth and under-bust girth. The M3 method determined bra cup size by measuring breast arc length. Conclusion:The researchers proposed an anthropometric bra cup sizing system with the breast arc length (M3 method). It was measured from the geometrically defined landmarks on the 3D body scan slicing contour lines. The new bra cup size was highly correlated with breast depth. Application:The noncontact measuring method used in this study can be applied to the ergonomic studies measuring sensitive body parts.

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“…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]). A large scale implementation of this statistical approach using data mining and decision trees was proposed in [15] and [2].…”

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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Development of a new chinese bra sizing system based on breast anthropometric measurements

Cited by 124 publications

References 18 publications

Anthropometry: An R Package for Analysis of Anthropometric Data

Anthropometry: An R Package for Analysis of Anthropometric Data

New Breast Measurement Technique and Bra Sizing System Based on 3D Body Scan Data

Apparel sizing using trimmed PAM and OWA operators

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