TY - JOUR
T1 - A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks
AU - Zhang, Jun
AU - Liang, Ruixin
AU - Lau, Newman
AU - Lei, Qiwen
AU - Yip, Joanne
N1 - Funding Information:
This research was supported by the Innovation and Technology Fund (Grant Number: ITS/243/16).
Funding Information:
This research was supported by the Innovation and Technology Fund (Grant Number: ITS/243/16) and the funder was Innovation and Technology Commission.
Publisher Copyright:
© 2022 by the authors.
PY - 2023/1
Y1 - 2023/1
N2 - 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.
AB - 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.
KW - backpropagation artificial neural network
KW - breast skin deformation
KW - computer-aided system
KW - gray relational analysis
UR - http://www.scopus.com/inward/record.url?scp=85145973753&partnerID=8YFLogxK
U2 - 10.3390/ijerph20010468
DO - 10.3390/ijerph20010468
M3 - Journal article
C2 - 36612790
AN - SCOPUS:85145973753
SN - 1661-7827
VL - 20
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
IS - 1
M1 - 468
ER -