Finite element-based machine learning method to predict breast displacement during running

Ruixin Liang, Joanne Yip, Winnie Yu, Lihua Chen, Newman Lau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
Pages (from-to)69-74
Number of pages6
JournalAATCC Journal of Research
Volume8
Issue numberSpecial Issue 1
DOIs
Publication statusPublished - 28 Mar 2021

Keywords

  • Breast Support
  • Computer Vision
  • Neural Network Simulation
  • Sports Bra

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Polymers and Plastics
  • Materials Chemistry

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