This paper introduces a co-design-based method for generating two-dimensional (2D) block patterns for physically disabled people with scoliosis, using three-dimensional (3D) virtual technology. A parameterization process is first performed on a scanned 3D body for creating a digitalized model of the human body, permitting simulation of the consumer's morphological shape with atypical physical deformations. Feature points of the human body for designing a garment block are discussed and classified with wearing ease for obtaining a desired fit effect based on the parameterized model. A basic garment block wire-frame aligned with body features is then established based on the defined feature points of the human body. Based on the deformed wireframe, a 3D expandable garment block is modeled. Customized 2D and 3D virtual garment prototyping tools are applied to create customized garment products based on the general concept of co-design by running the sequence Design–Display–Evaluation–Adjustment using the garment design process and design knowledge, which have already been applied to normal body shapes successfully. Through this process, the classical 2D garment design knowledge, especially 2D pattern design rules, will be modified according to the virtual garment evaluation procedure. The proposed method is validated and compared with the conventional block patternmaking methods in the virtual environment. The experimental results show that the proposed method is easier to implement and can generate garment patterns with satisfactory fit. Furthermore, the method can be used to create fit-ensured mass-customized apparel products (the top body type) for disabled people with scoliosis.

Using artificial intelligence to predict body dimensions rather than measuring them physically is a new research direction in apparel industry. If implemented, this technology can reduce costs and improve efficiency. In this paper, we proposed a back propagation artificial neural network (BP-ANN) model to predict pattern making-related body dimensions by inputting few key human body dimensions. In order to construct the proposed model, anthropometric measurements of 120 young females from the northeastern region of China were collected. The data were then used for training and the proposed model. The results showed that the prediction of the developed BP-ANN model is more accurate and stable than that of linear regression (LR) model. As great as the LR model was at pattern making, the BP-ANN model is even better. In the future, the precision of the proposed model can be further improved if the size of the learning data increases. The proposed method can be especially useful in making garment pattern for form-fitting clothing.

Purpose: The purpose of this paper is to find out the main factors that influence wearing comfort and how they influence garment-wearing comfort. Design/methodology/approach: Overall, 120 postures were extracted from the activities of daily life and work. Then, the numerical values of clothing pressure of these postures were measured using three-dimension virtual-reality technology. Finally, the data mining technology was applied to analyze the collected data. Findings: The wearing comfort of pants is mainly influenced by four factors – waist-hip factor, knee-shank factor, crotch factor and thigh-calf factor – and their contributions account for 39.17, 16.4, 13.96 and 6.95 percent, respectively. Hip, waist, crotch and knee influence wearing comfort significantly, and the part below the knee and the part of back thigh have no obvious effect on wearing comfort. Furthermore, the wearing comfort is acceptable if the numerical clothing pressures are below 20 kPa at the parts of hip, waist and crotch and below 10 kPa at the parts of back thigh, knee and shank. Originality/value: The paper demonstrates how different human body parts influence garment-wearing comfort. All of the results in this research facilitate pattern design of pants and quantitative evaluation of garment-wearing comfort.