Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94196
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Design | - |
dc.creator | Liang, R | en_US |
dc.creator | Yip, J | en_US |
dc.creator | Yu, W | en_US |
dc.creator | Chen, L | en_US |
dc.creator | Lau, N | en_US |
dc.date.accessioned | 2022-08-11T01:07:46Z | - |
dc.date.available | 2022-08-11T01:07:46Z | - |
dc.identifier.issn | 2472-3444 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/94196 | - |
dc.language.iso | en | en_US |
dc.publisher | American Association of Textile Chemists and Colorists | en_US |
dc.rights | © 2021 American Association of Textile Chemists and Colorists. | en_US |
dc.rights | 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. | en_US |
dc.subject | Breast support | en_US |
dc.subject | Computer vision | en_US |
dc.subject | Neural network simulation | en_US |
dc.subject | Sports bra | en_US |
dc.title | Finite element-based machine learning method to predict breast displacement during running | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 69 | en_US |
dc.identifier.epage | 74 | en_US |
dc.identifier.volume | 8 | en_US |
dc.identifier.doi | 10.14504/ajr.8.S1.9 | en_US |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | AATCC journal of research, 2021, v. 8, suppl. 1, p. 69-74 | en_US |
dcterms.isPartOf | AATCC journal of research | en_US |
dcterms.issued | 2021 | - |
dc.identifier.scopus | 2-s2.0-85121321526 | - |
dc.identifier.eissn | 2330-5517 | en_US |
dc.description.validate | 202208 bcrc | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1646 | - |
dc.identifier.SubFormID | 45744 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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