The relevance of breast motions and gaits in running exercises

• Published in: Fashion and textiles 2022, 9(1), , pp.1-21
• Main Authors: Zhou, Jie, Mao, Qian, Zhang, Jun, Lau, Newman M. L., Chen, Jianming
• Format: Article
• Language: English
• Subjects: Back propagation; Back propagation networks; Back-propagation neural network; Breast motions; Breasts; Business and Management; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Correlation analysis; Environment; Gait recognition; Gaits; Genetic algorithms; Life Sciences; Neural networks; Optimization algorithms; Parameters; Particle swarm optimization; Prediction models; Running; Running exercises; Textile Engineering; Underwear; 생활과학
• ISSN: 2198-0802; 2198-0802
• DOI: 10.1186/s40691-021-00283-4

The control of breast motions is a critical indicator to evaluate the comfort and function of sports bras. If the breast motions can be predicted based on the gait parameters detected by wearable sensors, it will more economical and convenient to evaluate the bras. Thirteen unmarried Chinese females with a breast cup of 75B were recruited in this study to investigate the regularity of breast motions and the relevance between breast motions and gaits during running exercises. The breast motion indicator is the distance alteration of breast regions. The gaits were described by the rotation angles of the hip, knee, ankle joints, and the foot height off the ground. Firstly, the Mann-Whitney U test and the Kruskal-Wallis H test were utilized to analyze the motion diversity among the eight breast regions. Then, the gray correlation analysis was applied to explore the relevance between breast motions and gaits. Finally, the back-propagation neural network, the genetic algorithm, and the particle swarm optimization algorithm were utilized to construct the prediction models for breast motions based on gait parameters. The results demonstrate that the same breast regions on the bilateral breasts and the different breast regions on the ipsilateral breasts present a significant motion diversity. There is a moderate correlation between breast motions and gait parameters, and the back-propagation neural network optimized by the particle swarm optimization algorithm performs better in breast motion prediction, which has a coefficient of determination of 84.58% and a mean absolute error of 0.2108.