2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 1 , PP: 16-30, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Hai Liu 1 * , Lei Hu 2
Doi: https://doi.org/10.54216/JISIoT.140102
Clothing design plays an important role in personal image expression and social and cultural transmission. The traditional fashion design method has many problems, such as low efficiency and large design error, and it is difficult to bring users better wearing experience. In order to meet different users’ Design needs, reduce design errors, and improve users’ satisfaction with design results, this paper combined with intelligent sensing technology, conducted in-depth research on digital automation analysis of clothing design CAD (Computer Aided Design). Aiming at the clothing design process, this paper first constructed a brand-new clothing design CAD system, using the depth transducer to solve the 3D information of the relevant feature points, and realized the accurate acquisition of the human body feature size information. Through the registration of adjacent frame point data, the 3D human body modeling was carried out. Then, according to the user’s physical characteristics and related information collected by the sensor, the paper compared the user’s characteristic information to filter out the user’s preferences, and used the recommendation algorithm to calculate the corresponding parameters to realize the intelligent choice of clothing styles. Finally, through the measurement of each index by the sensor, the size adjustment of the garment and the specific design of the garment were realized. In order to verify the effect of clothing design CAD system based on intelligent sensing technology, this paper conducted system tests. The results showed that in terms of clothing comfort, clothing quality and clothing functionality, the number of users satisfied and very satisfied reached 50.4%, 47.9% and 51.3%, respectively. From the overall survey results, the system has a high degree of user satisfaction. The research conclusion of this paper shows that the digital automatic analysis of clothing design CAD based on intelligent sensing technology can effectively meet the needs of users, improve their wearing experience, and promote the intelligent development of clothing design.
Costume Designing , Computer Aided Design , Intelligent Sensing Technology , Digitization and Automation , Wearing Experience
[1] Petrak, Slavenka, Maja Mahnic Naglic, and Dubravko Rogale. "Computer technology in fashion design and product development." Engineering Power: Bulletin of the Croatian Academy of Engineering 13.2 (2018): 22-24.
[2] Guo, Huijie, Boan Ying, Xin Zhang, Jing Qi. "Acquisition of Plate-making Process Knowledge for Smart Clothing CAD Systems." Journal of Fiber Bioengineering and Informatics 12.3 (2019): 137-145.
[3] Jankoska, Maja. "Application CAD methods in 3D clothing design." Tekstilna industrija 68.4 (2020): 31-37.
[4] Slavinskaya, Alla, Oksana Dombrovska, Viktoriia Mytsa, Julia Koshevko. "Method of control of the compatibility of the children’s clothing design using coefficients of dimentional features gradation." Vlakna a Textil 27.1 (2020): 76-86.
[5] Li, Xihang, Guiqin Li, Tiancai Li, Jianpin Lv, and Peter Mitrouchev. "Design of a multi-sensor information acquisition system for mannequin reconstruction and human body size measurement under clothes." Textile Research Journal 92.19 (2022): 3750-3765.
[6] Wei, Ding, Yukari Nagai, Liu Jing &Guo Xiao. "Designing comfortable smart clothing: for infants’ health monitoring." International Journal of Design Creativity and Innovation 7.1 (2019): 116-128.
[7] Tarafder, Nemailal. "CAD/CAM for Apparel Industry and Fashion Design." Journal of Industrial Mechanics (e-ISSN: 2582-1067) 6.1 (2021): 1-6.
[8] Chen, Lili, Zhou Rui. "Research on the application of collaborative learning in the practice teaching of garment 3D virtual fitting." Industria Textila 73.2 (2022): 113-120.
[9] Liu Qin, and Shang Xiaomei. "Research progress on clothing anthropometry technology." Modern silk science and Technology 34.6 (2019): 32-34.
[10] Lindell, David B., Matthew O’Toole, and Gordon Wetzstein. "Single-photon 3D imaging with deep sensor fusion." ACM Trans. Graph. 37.4 (2018): 113-125.
[11] Yang Saifeng, Gao Bin, Feng Shimin, Ding Enjie, Chen Qingfeng. "Human behavior recognition method based on three-dimensional bone nodes." Journal of Sensing Technology 31.12 (2018): 1834-1841.
[12] Wang Hongfu, Wang Yongbo, and Ke Ying. "Research progress on three-dimensional anthropometry technology." Journal of Clothing 3.6 (2018): 492-496.
[13] Zhang Xiaoniu, Wang Jun, and Zhang Chunyuan. "Review of three-dimensional human body modeling methods for digital clothing." Shandong Textile Technology 59.3 (2018): 44-47.
[14] Oh, Seolyoung, and Dongae Suh. "Mannequin fabrication methodology using 3D-scanning, modeling and printing." International Journal of Clothing Science and Technology 33.5 (2021): 683-695.
[15] Majhi, Santosh Kumar, and Shubhra Biswal. "Optimal cluster analysis using hybrid K-Means and Ant Lion Optimizer." Karbala International Journal of Modern Science 4.4 (2018): 347-360.
[16] Jahwar, Alan Fuad, and Adnan Mohsin Abdulazeez. "Meta-heuristic algorithms for K-means clustering: A review." PalArch’s Journal of Archaeology of Egypt/Egyptology 17.7 (2020): 12002-12020.
[17] Tao Ying, Yang Feng, Liu Yang, Dai Bing. "Research and optimization of K-means clustering algorithm." Computer technology and development 28.6 (2018): 90-92.
[18] Ghazal, Taher M. "Performances of K-means clustering algorithm with different distance metrics." Intelligent Automation & Soft Computing 30.2 (2021): 735-742.
[19] Nie, Feiping, Ziheng Li, Rong Wang, Xuelong Li. "An effective and efficient algorithm for K-means clustering with new formulation." IEEE Transactions on Knowledge and Data Engineering 35.4 (2022): 3433-3443.
[20] Musa-Ibrahim, A., and M. N. Agu. "An Assessment of the role of computer-aided design (CAD) in the fashion industry." International Journal of Innovation Science and Research Technology 6.3 (2021): 371-374.
[21] Hoque, Md Aynul, Rajah Rasiah, Fumitaka Furuoka, Sameer Kumar. "Technology adoption in the apparel industry: insight from literature review and research directions." Research Journal of Textile and Apparel 25.3 (2021): 292-307.
[22] Xia Haibin, Huang Hongyun, and Ding Zuohua. "Clothing comfort assessment based on migration learning and support vector machine." Journal of Textiles 41.6 (2020): 125-131.
