Rong Zheng

Aesthetic judgments are shaped by both visual properties and evaluative knowledge. However, it remains unclear how explicit expert-derived criteria relate to non-experts’ aesthetic judgments of clothing. This study compared non-experts’ judgments under guided and unguided evaluation procedures and examined their correspondence with expert evaluations. Seventeen criteria were developed through a three-round modified Delphi study. Twenty-six experts rated 48 clothing images. Of 60 non-experts randomly assigned to the guided or unguided condition, 59 were included in the analysis (29 guided, 30 unguided). Guided participants rated 17 criteria before giving an overall judgment; unguided participants provided overall ratings only. The guided condition was associated with lower overall aesthetic ratings and smaller absolute deviations from expert evaluations than the unguided condition. At the image level, guided judgments showed a stronger correlation with expert evaluations than unguided judgments (r = 0.771 vs. 0.222). Fifteen criteria showed positive within-person associations with overall judgments after multiple-testing correction, but only visual emphasis remained significant when all criteria were modeled together. These findings suggest that structured evaluation warrants further investigation. Differences in task demands and visual anchors prevent us from isolating the contribution of expert-derived criteria.

As the second skin of the human body, knitted clothing can provide the necessary thermal insulation, ensuring stable body temperature and comfortable sensations both during activity and rest. Given different thermoregulatory needs under these distinct physiological states, current study presents a double‐layered breathable knitted fabric with adaptive localized regulation. Its engineered structure could transform bio‐mechanical energy from motion into kinetic energy that drives mesh apertures, reducing thermal resistance to enhance heat dissipation. During rest, inherent elastic recovery closes the mesh, increasing thermal resistance to minimize the heat loss subsequently. Experimental results demonstrate exceptional fabric deformability and stretch recovery of DSBF (Double sides breathing fabric). When stretched to 140% strain, the air permeability increased to 300% of its original value. Meanwhile, obvious temperature variation between the inner and outer surfaces of garment occurs with the opening and closing of perforations, with the variation reaching 2.0°C. Infrared thermography tests confirm that, relative to the comparative sample, the modified garment increases the thermal resistance by 0.023 (K·m ² )/W at rest while decreasing it by 0.002 (K·m ² )/W during motion. Dynamic stretch‐breathable fabric panels enable bidirectional regulation of the garment's inner‐surface temperature, with changes ranging from ‐0.8°C–1.3°C between static and activ states. This corresponds to an estimated 12%–19.5% reduction in building energy consumption for climate‐controlled indoor environments.

Wearable pneumatic textiles hold significant potential for healthcare applications, particularly in preventing and treating stress‐related injuries and diseases. Current pneumatic healthcare wearables often suffer from undesired multidirectional expansion of air chambers, poor conformability to complex body contours, and difficulty in achieving controllable pressure gradients required for medical applications. To overcome these issues, herein, a Pneumatic Pressure‐Regulating Knitted Fabric (PPKF) was developed. Its design utilizes a knitted pneumatic circuit to seamlessly integrate an ultralow‐expansion pneumatic fiber within a dual‐channel structure. Ultralow radial expansion (≤ 1.41%) of pneumatic fiber enables controlled contractile deformation when embedded within knit structures. By strategically modulating channel spacing, PPKF achieves dynamic pressure modulation, delivering adjustable pressures (3–32 kPa) and clinically relevant gradient compression ratios (100.0%, 77.6%, 51.8%, 31.0%, 14.6%). PPKF‐based garments exhibit superior wearability, including breathability (949.56 mm/s), excellent fit, and minimal deformation (≤ 5.2%), outperforming commercially available alternatives. The integrated PPKF manufacturing strategy establishes a structure‐driven paradigm, providing new design principles for pressure‐regulating knitted textiles with enhanced wearability and multifunctionality. Substantial implementation potential is demonstrated across medical rehabilitation, aerospace systems, and athletic protection.

Real-time motion monitoring can have many significant applications in fields such as smart healthcare, human-computer interaction, sports training and fashion metaverse. However, current wearable systems used for motion monitoring suffer from issues related to usability, comfort, interactivity, and accuracy. To address these challenges, this work proposes a smart bodysuit system that integrates digital twin technology for real-time human motion monitoring and visualization. The system comprises novel hardware and software including a specially designed knitted bodysuit housing the electronic components to reduce calibration errors, a data calibration method to take into account the possible clothing-skeleton misalignment, an adaptive-threshold method combining random forest and zero-speed update algorithms to enhance motion recognition accuracy, and an enhanced TCP-Illinois congestion control algorithm to reduce system latency. Furthermore, digital twin technology is incorporated to improve human-computer interaction. Wearer trial tests showed that the system exhibits low latency, high accuracy, and good comfort and wearability. The network latency test showed a delay of 13.17 milliseconds, which is comparable to or even better than commercial systems. The testing error of the joint angle can be controlled within 0.2°, and the recognition accuracy of the motion pattern can reach 98.6%. The proposed adaptive-threshold method has higher positioning accuracy compared to the existing fixed-threshold method. Furthermore, the smart bodysuit system can reconstruct real-time wearer’s motion states through digital twin technology. The developed system is believed to have potential significant applications in the metaverse, digital healthcare, home monitoring, and sports science.

Personalization in the apparel industry shows importance and the potential for demand, but the existing personalization has unreasonable time cost, labor cost, and resource waste. To solve the problems of the waste of resources as well as both time and labor cost caused by manual pattern making in clothing personalization, a method of automatic garment pattern generation based on a parametric formula and the Python language was proposed. Based on the classification of common curves in patterns, three curve fitting algorithms based on different parameters were derived and combined with the Python language to achieve personalized generation of different patterns by classifying the parameters in the system into key parameters, secondary parameters, and variable parameters. Three different methods for verifying the accuracy of the garment patterns were proposed based on curve fitting similarity and three-dimensional virtual modeling, and the accuracy of the proposed system was verified. The results show that the accuracy and comfort of the patterns generated via the system were high. Meanwhile, the Python-language-based system fits well with the production system of enterprises, which can improve the rapid response capability of garment personalization, greatly save the time cost and labor cost of enterprises, reduce resource loss, and contribute to the sustainable development of the garment industry.

Waste recycling is an effective way to improve waste management, which is closely related to the support of social and economic foundations. With the development of a circular economy, green consumption is imperative. Most of the environmental protection brand enterprises are now almost limited to the environmental protection of clothing raw materials. However, there are still many problems in the overall industrial chain of the clothing industry, such as the pollution in the processes of processing, transportation and laundry, and the waste of resources caused by a large amount of textile waste after consumption. Starting from the theme of environmental protection and sustainable development of the clothing industry, this paper discusses the necessity of building a recycling system for post-consumer textile waste. Through the investigation of the recycling and reuse of domestic post-consumer textile waste, the existing problems are analyzed, such as the recycling supervision mechanism’s imperfections, the trust crisis and a lack of recycling channels. Combined with the successful cases abroad, some solutions and suggestions are put forward for the regeneration and reuse of post-consumer textile waste, and a preliminary conception of the charitable market system is made.

By examining the requirements of tunnel workers, multifunctional tunnel protective clothing was proposed in this study. The traditional tunnel protective clothing was redesigned from the perspective of improving clothing structure and fabric. A safe and protective clothing system, incorporating a harmful gas detection module, position monitoring module and data transmission module, was developed. Objective evaluation tests were carried out to verify the effectiveness of the proposed protective clothing. During the medium-intensity exercise phase, the average skin temperature and humidity of the participants who wore the new protective clothing were 1.0725 °C and 2.6% lower, respectively, than those who wore the conventional protective clothing (p < 0.05). Furthermore, the new protective clothing system exhibited a highly sensitive and complete feedback mechanism. The positioning error could be controlled within ±10 cm, when the horizontal spacing distance of the base station was set to 200 m.