This paper identifies the effective design features of commercial sports bras by evaluating the reduction of breast displacement during activities. Four subjects, with breast and body types representative of Chinese females of age 24–40 years were studied while they performed activities either braless or wearing one of seven different sports bras representative of the range commercially available. The three-dimensional movement of the breasts was derived by videoing the motion of breast markers attached at six different breast positions using a Vicon 3D motion analysis system. The breast displacement relative to the thorax was measured using a validated local “thorax–breast 3D coordinate system”. The results showed that there was a significant difference in breast displacement between the different breast markers. The bra samples generally achieved the greatest reduction in breast displacement in the medial–lateral direction. The reduction in breast displacement was positively related to the gore height, shoulder strap width, neckline height and side seam depth. The underlying mechanism of breast movement and bra support is also discussed. The study concluded that the most effective bras had the following features: compression type, short vest style, high neckline, slings, cross back, bound neckline, no centre gore, no wire, no cradle, no pad and a non-adjustable wide strap. This provides practical guidelines for bra designers and manufacturers to develop more supportive bras for women performing these physical activities.

This paper presents a new method for evaluating 3D breast motion relative to the thorax using a new breast coordinate system. A local virtual origin is defined by four breast boundary points, in a constant relative distance from four bone-fixed thoracic points. The validity and reliability of the method was tested with 11 subjects and confirmed (interclass correlation coefficient = 1.00). The feasibility of the new system was tested with two subjects with pert and ptotic breasts, respectively. Each subject participated in motion experiments whilst treadmill jogging, with and without a sports bra. In the braless motion experiments, the breast movement trajectories of six breast positions resembled a butterfly shape. In a jogging cycle, there was a single anterior and medial peak, and a double peak in the vertical breast displacement. The ptotic breast displacement during jogging was larger and wavy, with a more evident vertical and medial-lateral vibration. It tended to move more medially and anteriorly than the pert breast. The bra trajectories of the subjects wearing sports bras showed that the superior movements of the upper breast need further control. To achieve this, it was proposed that the neckline of the bra should better fit the upper breast boundary.

To facilitate a theoretical analysis of breast motion to enable the design of more supportive bras, a dynamic FEM based on reliable non-linear properties of breast tissues has been developed. The methods and findings have potential widespread benefit for developing new products to promote women's health and comfort.

Rich in variety, jacquard fabric can be divided into four categories in terms of weaving structures: single-layer, backed structure (weft-backed, warp-backed), double-layer and multi-layer [1]. The process of designing traditional jacquard begins with the object that has been handled through freehand design on pattern and color, and the purpose of the structural design for the jacquard fabric is to copy the effect of patterns and colors [2]. However, limited by the craftsmanship of freehand, the design of traditional jacquard has always been a passive process -a mode of artistic design combined with mechanical imitation, whose key lies in structural design that embodies the unique technique and artistic features of jacquard fabric. Although the application of the jacquard CAD system can improve the efficiency of structural design at present, the jacquard CAD system per se was but to assist the structural design of jacquard [3]. The object of design still relies its prominence on free handwork that enjoys no further innovation in its design concept. Thus, this improved approach has not been able to bring about the advantage of innovative digital technology. 1 Digital jacquard technology includes jacquard CAD technology and digital production technology represented by electronic jacquard machine and new-generation weaving looms. The design and production processes are both subject to total digital control, of which the data of the jacquard fabric from design to weaving are all processed, controlled and transmitted in the computer [4]. It provides a technological basis for the innovative design of digital jacquard. Provided that technical constraints such as freehand are removed to make jacquard processing directly availaAbstract Digital jacquard fabric has its design concept and method directly borrowed from computer images and color modes. This has enabled the creation of innovative jacquard fabric design that is far beyond what freehand patterns can express. In this paper, the principles of digital jacquard designing have been classified into two parts: colorless mode and colorful mode, and an innovative layered-combination design mode has been suggested that contains this new design concept, by which digital jacquard fabric can be designed from colorless single-layer structures to colorful compound structures. As a result, designed jacquard fabrics are capable of expressing picturesque and photo-realistic effects with a mega-level color number on the face of the fabric. It is envisaged that the results of the study are of tremendous benefit to the creation of new jacquard fabrics with inimitable digital effects that can be manufactured.

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A Genetic Algorithm-Back Propagation (GA-BP) neural network method has been proposed to predict the clothing pressure of girdles in different postures. Firstly, a Back Propagation (BP) neural network model was used to predict the clothing pressure based on seven parameters, and three optimal functions of the model were derived. However, the prediction error 0.85411 of the network was more than the forecast requirement of 0.5 and the optimal initial weights and thresholds for the network could not be calculated. Therefore, a GA model and the BP neural network model were combined into a new GA-BP neural network model, which was used to predict the clothing pressure based on the three optimal functions. The results showed that the prediction error for this GA-BP neural network model was 0.41652, which was less than the forecast requirement of 0.5. Hence, the model was shown to predict the girdle pressure with acceptable accuracy. Finally, the internal calculation function equation for the GA-BP neural network was derived.

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