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.

Miao embroidery holds significant cultural, economic, and aesthetic value. However, its transmission faces numerous challenges: a high learning threshold, a lack of interest among younger generations, and low production efficiency. These factors have created obstacles to its sustainable development. In the age of artificial intelligence (AI), generative AI is expected to improve the efficiency of pattern innovation and the adaptability of the embroidery industry. Therefore, this study proposes a Miao embroidery pattern generation and application method based on Stable Diffusion and low-rank adaptation (LoRA) fine-tuning. The process includes image preprocessing, data labeling, model training, pattern generation, and embroidery production. Combining objective indicators with subjective expert review, supplemented by feedback from local artisans, we systematically evaluated five representative Miao embroidery styles, focusing on generation quality and their social and business impact. The results demonstrate that the proposed model outperforms the original diffusion model in terms of pattern quality and style consistency, with optimal results obtained under a LoRA scale of 0.8–1.2 and diffusion steps of 20–40. Generated patterns were parameterized and successfully implemented in digital embroidery. This method uses AI technology to lower the skill threshold for embroidery training. Combined with digital embroidery machines, it reduces production costs, significantly improving productivity and increasing the income of embroiderers. This promotes broader participation in embroidery practice and supports the sustainable inheritance of Miao embroidery. It also provides a replicable technical path for the intelligent generation and sustainable design of intangible cultural heritage (ICH).

Nowadays, the popularity of the internet has continuously increased. Predicting human body dimensions intelligently would be beneficial to improve the precision and efficiency of pattern making for enterprises in the apparel industry. In this study, a new predictive model for estimating body dimensions related to garment pattern making is put forward based on radial basis function (RBF) artificial neural networks (ANNs). The model presented in this study was trained and tested using the anthropometric data of 200 adult males between the ages 20 and 48. The detailed body dimensions related to pattern making could be obtained by inputting four easy-to-measure key dimensions into the RBF ANN model. From the simulation results, when spreading parameter σ and momentum factor α were set to 0.012 and 1, the three-layer model with 4, 72, and 8 neurons in the input, hidden, and output layers, respectively, showed maximum accuracy, after being trained by a dataset with 180 samples. Moreover, compared with a classic linear regression model and the back propagation (BP) ANN model according to mean squared error, the predictive performance of the RBF ANN model put forward in this study was better than the other two models. Therefore, it is feasible for the presented predictive model to design garment patterns, especially for tight-fitting garment patterns like activewear. The estimating accuracy of the proposed model would be further improved if trained by more appropriate datasets in the future.

This study investigated the effect of sports bra type (encapsulation versus compression) and gait speed on perceptions of breast discomfort, bra discomfort and breast movement reported by Chinese women. Visual analogue scales were used to evaluate breast discomfort, bra component discomfort and perceived breast movement of 21 Chinese participants when they wore an encapsulation or a compression sports bra, while static and while exercising at three different gait speeds. Participants perceived less breast discomfort and breast movement when wearing a compression bra compared to an encapsulation bra at a high gait speed, suggesting that compression bras are likely to provide the most effective support for Chinese women. However, significantly higher bra discomfort was perceived in the compression bra compared to the encapsulation bra when static and at the lower gait speed, implying that ways to modify the design of sports bras, particularly the straps, should be investigated to provide adequate and comfortable breast support. Practitioner Summary: The compression sports bra provided more comfortable support than the encapsulation sports bra for these Chinese women when running on a treadmill. However, these women perceived higher bra discomfort when wearing the compression bra when stationary. Further research is needed to modify the design of sports bras, particularly the straps, to provide adequate and comfortable breast support.