Analysis of pressure distribution during movement for the top part of female socks. Issue 11 (June 2020)

• Record Type: Journal Article
• Authors: Yan, Xiang-hui; Wang, Li-jun; Wang, Ming-ling; Shi, Jia-ling
• Is Part Of: Textile research journal. Volume 90:Issue 11/12(2020)
• Page Start: 1301
• Page End: 1310
• Publication Date: 2020-06
• Subjects: female sports socks; dynamic pressure of socks' top part; movement velocity; leg circumference; multiple regression model; BP neural network model; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/0040517519888826
• Languages: English
• ISSNs: 0040-5175

Abstract

Female sports socks were studied to achieve the correlation between the ankle surface curvature and pressure distribution of the top part of socks. The transverse tension performance of the socks' top part was obtained using an Instron universal strength tester, and the leg size was measured with a [TC] 2 contactless 3D body scanner. The pressure was monitored by a Pliance-X-32 pressure test system. Gray correlation, variance, and regression analysis were applied to study the correlation between movement velocity, fabric performance, leg circumference, and ankle pressure distribution. The dynamic pressure prediction models of multiple regression and back propagation (BP) neural network on the top part of socks were also established. The results show that the transverse tension performance and sock density have a significant effect on the ankle static pressure. Movement velocity, sock density, and leg circumference are positively correlated with dynamic pressure, while the elastic recovery rate of the fabric is negatively correlated with the pressure. Both of the multiple regression and BP neural network models can predict the dynamic pressure, and the BP neural network model is better than multiple regression at prediction error, which was kept to less than 0.5%. Therefore, the BP neural network model can be effectively used in female ribbed sock top design.