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.
| Original language | English |
|---|---|
| Article number | 468 |
| Journal | International Journal of Environmental Research and Public Health |
| Volume | 20 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- backpropagation artificial neural network
- breast skin deformation
- computer-aided system
- gray relational analysis
ASJC Scopus subject areas
- Pollution
- Public Health, Environmental and Occupational Health
- Health, Toxicology and Mutagenesis
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