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The level of thermal-moisture comfort experienced by an individual is strongly influenced by the clothing they wear and also influenced by the ease allowance of that clothing. This study aimed to investigate the impact of ease allowances of clothing on thermal-moisture comfort during exercise or movement. Seventeen female subjects were asked to wear sample clothing with different sizes during a designated 5-phase exercise in a climate chamber. Throughout the exercise phases, the temperature and humidity of the microclimate under the clothing were monitored while the subjects provided subjective evaluations of their comfort levels. To account for variation in ease allowances, the 32 experimental repetitions were grouped based on the ease allowance of the clothing worn. The FCE-CRITIC method was then used to determine the variable weights of factors and to compare the thermal-moisture status to the subjective evaluation. Results revealed that humidity was more strongly weighted during RS and RC (i.e., running and recovery phase after exercise) compared to other phases, means that moisture comfort was the main factor driving the clothing comfort sensation within the two phases. The ease allowance of clothing was found to have an impact on the sensations of thermal and moisture comfort during exercise, although this effect was not significant. Furthermore, the study identified the optimum range of ease allowance for thermal-moisture comfort during exercise to be between 6 and 10 cm.

Nowadays, the safety of special children has gradually become a hot spot of social concern. In this paper, aiming at the problems of autism spectrum disorder children who are easy to get lost and drown, intelligent monitoring clothing with lost warning and drowning rescue functions is designed. Firstly a clothing technology system based on multi-MCU and BeiDou dual-mode positioning was set up. Then according to their characteristics of physiology and psychology, CLO was used for virtual simulation design of color, pattern, and style of sample clothes. Finally, the product prototype of intelligent monitoring clothing was evaluated from various aspects. The results confirm that the clothing can meet the physical and psychological needs of these children. Its intelligent sensor positioning, data acquisition and transmission, and waterproof and rescue functions are designed accurately, which can not only play an early warning and protection role when children go beyond the fence and drown, but also reduce the monitoring burden of parents to a certain extent. This study also provides a reference for the design of intelligent monitoring clothing for special children.

With the development of online shopping platforms, consumers and designers need to choose from a large number of garments when shopping or designing. Quick identification of clothing products can effectively improve the efficiency of designers’ and consumers’ experience. Therefore, this paper used DeepLabV3+ combined with deep separable convolution to improve the network computation speed. To address the problem of low recognition rate of H-shaped silhouette in semantic segmentation, the fuzzy trouser silhouette samples are further analyzed. The trouser silhouette was redefined according to the characteristics of pants, and the dataset labels were optimized with a trouser silhouette classification method. It was found that the accuracy and efficiency of trouser silhouette recognition were significantly improved. The indicators of recall rate, IoU and PA of H silhouette is improved by 6%, 5%, and 1% respectively. After label optimization, the classification prediction accuracy of silhouette V is 100%, the recall of silhouette V is 97%, and the recall of silhouette O is 96%.