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A novel evaluation technique for human body perception of clothing fit

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Abstract

Fit evaluation plays an important role in garment products development and sales. Effective clothing fit evaluation methods can reduce the development cost of apparel products and the return rate of online apparel sales. In this research, we proposed an intelligent fit evaluation technology to predict clothing fit. The mathematical relationship model between clothing fit levels and indexes reflecting the clothing fit levels was constructed by using decision tree C4.5 algorithm. Then, two experiments were carried out to collect input and output training data. After learning from the collected data, the proposed model can predict clothing fit accurately. Next, we validated our proposed model’s prediction accuracy using K-fold cross validation. Finally, we gave two applications of the proposed model for clothing products development and shopping online. Results show that our proposed method has high prediction accuracy and less requirement for the number of learning samples, and can predict clothing fit automatically and rapidly without real try-on.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

References

  1. Ashdown SP, Dunne L (2006) A study of automated custom fit: readiness of the technology for the apparel industry. Cloth Text Res J 24:121–136

    Article  Google Scholar 

  2. Chattaraman V, Simmons KP, Ulrich PV (2013) Age, body size, body image, and fit preferences of male consumers. Cloth Text Res J 31:291–305

    Article  Google Scholar 

  3. Chen C-M (2007) Fit evaluation within the made-to-measure process. Int J Cloth Sci Technol 19:131–144

    Article  Google Scholar 

  4. Chen X, Tao X, Zeng X, Koehl L, Boulenguez-Phippen J (2015) Control and optimization of human perception on virtual garment products by learning from experimental data. Knowl-Based Syst 87:92–101

    Article  Google Scholar 

  5. Chiew KL, Tan CL, Wong K, Yong KS, Tiong WK (2019) A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Inf Sci 484:153–166

    Article  Google Scholar 

  6. Dutt A, Ismail MA, Herawan T (2017) A systematic review on educational data mining. IEEE Access 5:15991–16005

    Article  Google Scholar 

  7. Guo Z, Wong W, Leung S, Li M (2011) Applications of artificial intelligence in the apparel industry: a review. Text Res J 81:1871–1892

    Article  Google Scholar 

  8. Han L, Li W, Su Z (2019) An assertive reasoning method for emergency response management based on knowledge elements C4.5 decision tree. Expert Syst Appl 122:65–74

    Article  Google Scholar 

  9. Huck J, Maganga O, Kim Y (1997) Protective overalls: evaluation of garment design and fit. Int. J. Cloth. Sci. Technol. 9:45–61

    Article  Google Scholar 

  10. Jiang Q, Jin X, Lee S-J, Yao S (2019) A new similarity/distance measure between intuitionistic fuzzy sets based on the transformed isosceles triangles and its applications to pattern recognition. Expert Syst Appl 116:439–453

    Article  Google Scholar 

  11. Kausar S, Huahu X, Hussain I, Wenhao Z, Zahid M (2018) Integration of data mining clustering approach in the personalized E-learning system. IEEE Access 6:72724–72734

    Article  Google Scholar 

  12. Kim H, Damhorst ML (2010) The relationship of body-related self-discrepancy to body dissatisfaction, apparel involvement, concerns with fit and size of garments, and purchase intentions in online apparel shopping. Cloth Text Res J 28:239–254

    Article  Google Scholar 

  13. Lin Y-L, Wang M-JJ (2015) The development of a clothing fit evaluation system under virtual environment. Multimed Tools Appl 75:7575–7587

    Article  Google Scholar 

  14. Liu K, Kamalha E, Wang J, Agrawal T-K (2016) Optimization Design of Cycling Clothes' patterns based on digital clothing pressures. Fiber Polym 17:1522–1529

    Article  Google Scholar 

  15. Liu K, Wang J, Zhu C, Hong Y (2016) Development of upper cycling clothes using 3D-to-2D flattening technology and evaluation of dynamic wear comfort from the aspect of clothing pressure. Int J Cloth Sci Technol. 28:736–749

    Article  Google Scholar 

  16. Liu K, Wang J, Zeng X, Tao X, Bruniaux P, Edwin K (2016) Fuzzy classification of young women's lower body based on anthropometric measurement. Int J Ind Ergonom 55:60–68

    Article  Google Scholar 

  17. Liu K, Wang J, Hong Y (2017) Wearing comfort analysis from aspect of numerical garment pressure using 3D virtual-reality and data mining technology. Int J Cloth Sci Technol 29:166–179

    Article  Google Scholar 

  18. Liu K, Zeng X, Bruniaux P, Wang J, Kamalha E, Tao X (2017) Fit evaluation of virtual garment try-on by learning from digital pressure data. Knowl-Based Syst 133:174–182

    Article  Google Scholar 

  19. Liu K, Wang J, Kamalha E, Li V, Zeng X (2017) Construction of a body dimensions' prediction model for garment pattern making based on anthropometric data learning. J Text Inst 108:2107–2114

    Article  Google Scholar 

  20. Liu K, Wang J, Zhu C, Kamalha E, Hong Y, Zhang J, Dong M (2017) A mixed human body modeling method based on 3d body scanning for clothing industry. Int J Cloth Sci Technol 29:673–685

    Article  Google Scholar 

  21. Liu K, Zeng X, Bruniaux P, Tao X, Yao X, Li V, Wang J (2018) 3D interactive garment pattern-making technology. Comput Aided Des 104:113–124

    Article  Google Scholar 

  22. Liu K, Zeng X, Wang J, Tao X, Xu J, Jiang X, Ren J, Kamalha E, Agrawal T-K, Bruniaux P (2018) Parametric design of garment flat based on body dimension. Int J Ind Ergonom 65:46–59

    Article  Google Scholar 

  23. Liu K, Wu H, Zhu C, Wang J, Zeng X, Tao X, Bruniaux P (2022) An evaluation of garment fit to improve customer body fit of fashion design clothing. Int J Adv Manuf Tech 120:2685–2699

    Article  Google Scholar 

  24. Lu Y, Song G, Li J (2014) A novel approach for fit analysis of thermal protective clothing using three-dimensional body scanning. Appl Ergon 45:1439–1446

    Article  Google Scholar 

  25. Melin P, Sanchez D (2018) Multi-objective optimization for modular granular neural networks applied to pattern recognition. Inf Sci 460:594–610

    Article  MathSciNet  Google Scholar 

  26. Satam D, Liu Y, Lee HJ (2011) Intelligent design systems for apparel mass customization. J Text Inst 102:353–365

    Article  Google Scholar 

  27. Shin E, Baytar F (2014) Apparel fit and size concerns and intentions to use virtual try-on: impacts of body satisfaction and images of models’ bodies. Cloth Text Res J 32:20–33

    Article  Google Scholar 

  28. Sun P, Li J, Bhuiyan MZA, Wang L, Li B (2019) Modeling and clustering attacker activities in IoT through machine learning techniques. Inf Sci 479:456–471

    Article  Google Scholar 

  29. Tao X, Bruniaux P (2013) Toward advanced three-dimensional modeling of garment prototype from draping technique. Int J Cloth Sci Technol 25:266–283

    Article  Google Scholar 

  30. Tao X, Chen X, Zeng X, Koehl L (2018) A customized garment collaborative design process by using virtual reality and sensory evaluation on garment fit. Comput Ind Eng 115:683–695

    Article  Google Scholar 

  31. Thomassey S, Bruniaux P (2013) A template of ease allowance for garments based on a 3D reverse methodology. Int J Ind Ergonom 43:406–416

    Article  Google Scholar 

  32. Zhang X, Yeung K, Li Y (2002) Numerical simulation of 3D dynamic garment pressure. Text Res J 72:245–252

    Article  Google Scholar 

  33. Zhao X, Fan K, Shi X, Liu K (2021) Virtual fit evaluation of pants using the adaptive network fuzzy inference system. Text Res J 91:2786–2794

    Article  Google Scholar 

Download references

Funding

This paper was financially supported by the National Natural Science Foundation of China (No. 61806161), the Natural Science Basic Research Program of Shaanxi Province, China (No. 2019JQ-848), the Innovation Ability Support Plan of Shaanxi Province-young Science and Technology Star Project, China (No. 2020KJXX-083), China and the Youth Innovation Team of Shaanxi Universities, China.

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Authors and Affiliations

  1. Apparel and Art Design College, Xi’an Polytechnic University, Xi’an, 710048, China

    Kaixuan Liu & Chun Zhu

  2. Shanxi Key Laboratory of Intelligent Clothing Design, Xi’an Polytechnic University, Xian, 710048, China

    Kaixuan Liu

  3. GEMTEX Laboratory, ENSAIT, 59100, Roubaix, France

    Kaixuan Liu, Xuyuan Tao, Pascal Bruniaux & Xianyi Zeng

  4. College of Fashion and Design, Donghua University, Shanghai, 200051, China

    Chun Zhu & Jianping Wang

Authors
  1. Kaixuan Liu
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  2. Chun Zhu
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  3. Xuyuan Tao
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  4. Pascal Bruniaux
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  5. Xianyi Zeng
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  6. Jianping Wang
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Correspondence to Kaixuan Liu.

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Liu, K., Zhu, C., Tao, X. et al. A novel evaluation technique for human body perception of clothing fit. Multimed Tools Appl 82, 21057–21069 (2023). https://doi.org/10.1007/s11042-023-14530-x

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  • DOI: https://doi.org/10.1007/s11042-023-14530-x

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