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http://hdl.handle.net/10397/94196
Title: | Finite element-based machine learning method to predict breast displacement during running | Authors: | Liang, R Yip, J Yu, W Chen, L Lau, N |
Issue Date: | 2021 | Source: | AATCC journal of research, 2021, v. 8, suppl. 1, p. 69-74 | Abstract: | This paper presents an effective method to simulate the dynamic deformation of the breasts when a sports bra is worn during physical activity. A subject-specific finite element (FE) model of a female subject is established, and the accuracy of the material coefficients of the model is analyzed. An FE model of the sports bra is also built based on a commercially-available compression sports bra with a vest style. Then, an FE contact model between the body and bra is developed and validated, and the results applied to train a neural network model for predicting breast displacement based on bra straps with different tensile moduli. In this study, a four-layer neural network with a backpropagation algorithm (a Levenberg-Marquardt learning algorithm) is used. A comparison of the FE and machine learning results shows that machine learning can well predict the dynamic displacement of the breasts in a more time-efficient and convenient manner. | Keywords: | Breast support Computer vision Neural network simulation Sports bra |
Publisher: | American Association of Textile Chemists and Colorists | Journal: | AATCC journal of research | ISSN: | 2472-3444 | EISSN: | 2330-5517 | DOI: | 10.14504/ajr.8.S1.9 | Rights: | © 2021 American Association of Textile Chemists and Colorists. This is the accepted version of the publication Liang R, Yip J, Yu W, Chen L, Lau N. Finite Element-Based Machine Learning Method to Predict Breast Displacement during Running. AATCC Journal of Research. 2021;8(1_suppl):69-74.Copyright © 2021 (American Association of Textile Chemists and Colorists). DOI:10.14504/ajr.8.S1.9. |
Appears in Collections: | Journal/Magazine Article |
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