A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks

Jun Zhang, Ruixin Liang (), Newman Lau, Qiwen Lei and Joanne Yip ()
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Jun Zhang: School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
Ruixin Liang: Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong, China
Newman Lau: School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
Qiwen Lei: School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
Joanne Yip: School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China

IJERPH, 2022, vol. 20, issue 1, 1-18

Abstract: The measurement and prediction of breast skin deformation are key research directions in health-related research areas, such as cosmetic and reconstructive surgery and sports biomechanics. However, few studies have provided a systematic analysis on the deformations of aging breasts. Thus, this study has developed a model order reduction approach to predict the real-time strain of the breast skin of seniors during movement. Twenty-two women who are on average 62 years old participated in motion capture experiments, in which eight body variables were first extracted by using the gray relational method. Then, backpropagation artificial neural networks were built to predict the strain of the breast skin. After optimization, the R-value for the neural network model reached 0.99, which is within acceptable accuracy. The computer-aided system of this study is validated as a robust simulation approach for conducting biomechanical analyses and predicting breast deformation.

Keywords: breast skin deformation; backpropagation artificial neural network; gray relational analysis; computer-aided system (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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