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Research on winter sportswear comfort and its visual model

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Abstract

In order to study the comfort of tight-fitting sportswear in winter, this paper designed a series of motions, and explored the distribution of comfort perception under different sports conditions by evaluating the wearing perception of human body. Finally, through the acquired experimental data, intelligent prediction models were established, and the prediction results were visualized, which makes the comfort distribution more intuitive. The results show that there are great differences in the parts that affect the overall comfort perception under different sports conditions; different ages subjects have different perceptions of comfort; Particle Swarm Optimization-Cuckoo Search-Adaptive Network-based Fuzzy Inference System has better prediction accuracy, and could replace the wearing trials.

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References

  1. Al-Adwan, A.S., Albelbisi, N.A., Hujran, O., Al-Rahmi, W.M., Alkhalifah, A.: Developing a holistic success model for sustainable E-learning: a structural equation modeling approach. Sustainability 13(16), 9453 (2021)

    Google Scholar 

  2. Atalie, D., Gideon, R.K., Ferede, A., Tesinova, P., Lenfeldova, I.: Tactile comfort and low-stress mechanical properties of half-bleached knitted fabrics made from cotton yarns with different parameters. J. Nat. Fibers 18(11), 1699–1711 (2019)

    Google Scholar 

  3. Atasağun, H.G., Okur, A., Psikuta, A., Rossi, R.M., Annaheim, S.: The effect of garment combinations on thermal comfort of office clothing. Text. Res. J. 89(21–22), 4425–4437 (2019)

    Google Scholar 

  4. Barker, R., Bernard, A., Hinks, D., Liston, G., Jones, C., Singleton, S.: Factors affecting human tactile response to wash-treated garments: analysis of fabric and garment effects in dynamic wear. AATCC J. Res. 1(1), 13–23 (2014)

    Google Scholar 

  5. Basra, S.A., Azam, Z., Asfand, N., Anas, S., Iftikhar, K., Irshad, M.A.: Development of interlock knitted seersucker fabric for better comfort properties. J. Eng. Fibers Fabr. 15, 1–8 (2020)

    Google Scholar 

  6. Britto, D., Al, L., Cs, D.S., et al.: Effect of a compressive garment on kinematics of jump-landing tasks. J. Strength Cond. Res. 31(9), 2480–2488 (2016)

    Google Scholar 

  7. Çeven, E.K., Günaydın, G.K.: Evaluation of some comfort and mechanical properties of knitted fabrics made of different regenerated cellulosic fibres. Fibers Polym. 22(2), 567–577 (2021)

    Google Scholar 

  8. Chen, J.F., Do, Q.H., Hsieh, H.N.: Training artificial neural networks by a hybrid PSO-CS algorithm. Algorithms 8(2), 292–308 (2015)

    MathSciNet  MATH  Google Scholar 

  9. Cheng, P., Chen, D., Wang, J.: Study on the influence of underwear on local thermal and moisture comfort of human body. Therm. Sci. 25(4 Part A), 2589–2608 (2021)

    Google Scholar 

  10. Cheng, P., Chen, D., Wang, J.: Clustering of the body shape of the adult male by using principal component analysis and genetic algorithm–BP neural network. Soft. Comput. 24(17), 13219–13237 (2020)

    Google Scholar 

  11. Choi, J., Hong, K.: 3D skin length deformation of lower body during knee joint flexion for the practical application of functional sportswear. Appl. Ergon. 48, 186–201 (2015)

    Google Scholar 

  12. Cotter, J.D., Patterson, M.J., Taylor, N.A.: The topography of eccrine sweating in humans during exercise. Eur. J. Appl. Physiol. 71(6), 549–554 (1995)

    Google Scholar 

  13. Daukantiene, V., Vadeike, G.: Evaluation of the air permeability of elastic knitted fabrics and their assemblies. Int. J. Cloth. Sci. Technol. 30(6), 839–853 (2018)

    Google Scholar 

  14. Ding, W., Wang, J., Wang, J.: Multigranulation consensus fuzzy-rough based attribute reduction. Knowl. Based Syst. 198, 105945 (2020)

    Google Scholar 

  15. Doan, B., Kwon, Y.H., Newton, R., Shim, J., Popper, E.V.A., et al.: Evaluation of a lower-body compression garment. J. Sports Sci. 21(8), 601–610 (2003)

    Google Scholar 

  16. Ertekin, G., Ertekin, M., Marmaralı, A.: Visual perception and performance properties of fabrics knitted with elastic core cotton slub yarns. J. Nat. Fibers 19(3), 810–822 (2022)

    Google Scholar 

  17. Eryuruk, S.H.: The effects of elastane and finishing processes on the performance properties of denim fabrics. Int. J. Cloth. Sci. Technol. 31(2), 243–258 (2019)

    Google Scholar 

  18. Ghodrati, A., Lotfi, S.: A hybrid CS/PSO algorithm for global optimization. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) Asian Conference on Intelligent Information and Database Systems, pp. 89–98. Springer, Berlin, Heidelberg (2012)

    Google Scholar 

  19. Gu, C., Shu, Y.H.: Researchondynamicheat-moisturecomfortofsports-wearfabrics. J. Anhui Polytech. Univ. 25(03), 15–19 (2010)

    Google Scholar 

  20. Gu, M., Sun, Y., Song, C.: Study on factors effecting comfortability of inside garment microclimate. Shandong Text. Sci. Technol. 4, 51–54 (2008)

    Google Scholar 

  21. Hertzman, A.B.: Individual differences in regional sweating. J. Appl. Physiol. 10(2), 242–248 (1957)

    Google Scholar 

  22. Jalal, M., Ramezanianpour, A.A., Pouladkhan, A.R., Tedro, P.: Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders. Neural Comput. Appl. 23(2), 455–470 (2013)

    Google Scholar 

  23. Kaplan, S., Yilmaz, B.: Thermal comfort performances of double-face knitted insulation fabrics. Fibers Polym. 23(2), 537–545 (2022)

    Google Scholar 

  24. Kara, S.: Comparison of sewn fabric bending rigidities: effects of different stitch types and seam directions. Ind. Text. 71(2), 105–111 (2020)

    Google Scholar 

  25. Karasawa, Y., Uemae, M., Yoshida, H., Kamijo, M.: Effectiveness of a method of evaluating the clothing comfort sensation in a perspiration state by measuring psychophysiological responses. Int. J. Affect. Eng. 20(1), 21–31 (2021)

    Google Scholar 

  26. Kuang, C., Chen, Y.: Research on evaluation method of fabric tactile sensibility. Biotechnol. Indian J. 10(18), 10431–10437 (2014)

    Google Scholar 

  27. Kurek, K.A., Heijman, W., van Ophem, J., Gędek, S., Strojny, J.: Measuring local competitiveness: comparing and integrating two methods PCA and AHP. Qual. Quant. 56(3), 1371–1389 (2022)

    Google Scholar 

  28. Li, Y., Keighley, J.H., Mclntyre, J.E., Hampton, I.: Predictability between objective physical factors of fabrics and subjective preference votes for derived garments. J. Text. Inst. 82(3), 277–284 (1991)

    Google Scholar 

  29. Liu, S., Cui, W., Wu, Y., Liu, M.: A survey on information visualization: recent advances and challenges. Vis. Comput. 30(12), 1373–1393 (2014)

    Google Scholar 

  30. Machado-Moreira, C.A., Smith, F.M., van den Heuvel, A.M., Mekjavic, I.B., Taylor, N.A.: Sweat secretion from the torso during passively-induced and exercise-related hyperthermia. Eur. J. Appl. Physiol. 104(2), 265–270 (2008)

    Google Scholar 

  31. Maji, P., Garai, P.: On fuzzy-rough attribute selection: criteria of max-dependency, max-relevance, min-redundancy, and max-significance. Appl. Soft Comput. 13(9), 3968–3980 (2013)

    Google Scholar 

  32. Mert, E., Böhnisch, S., Psikuta, A., Bueno, M.A., Rossi, R.M.: Contribution of garment fit and style to thermal comfort at the lower body. Int. J. Biometeorol. 60(12), 1995–2004 (2016)

    Google Scholar 

  33. Mjahed, M., Ayad, H.: Quadrotor identification through the cooperative particle swarm optimization-cuckoo search approach. Comput. Intell. Neurosci. 2019, 1–10 (2019)

    Google Scholar 

  34. Moshagen, M., Auerswald, M.: On congruence and incongruence of measures of fit in structural equation modeling. Psychol. Methods 23(2), 318–336 (2018)

    Google Scholar 

  35. Mousavi, G., Varsei, M., Rashidi, A., Ghazisaeidi, R.: Experimental evaluation of the compression garment produced from elastic spacer fabrics through real human limb. J. Ind. Text. (2021). https://doi.org/10.1177/1528083720988089

    Article  Google Scholar 

  36. Narges, S., Ghorban, A., Hassan, K., Mohammad, K.: Prediction of the optimal dosage of coagulants in water treatment plants through developing models based on artificial neural network fuzzy inference system (ANFIS). J. Environ. Health Sci. Eng. 19(2), 1543–1553 (2021)

    Google Scholar 

  37. Ning, H.: Study on Human Thermal Comfort and Thermal Adaptation in Cold District Heating Building Environment. Harbin Institute of Technology, Harbin (2017)

    Google Scholar 

  38. Pandit, A., Panda, S.: Prediction of earthquake magnitude using soft computing techniques: ANN and ANFIS. Arab. J. Geosci. 14(13), 1–10 (2021)

    Google Scholar 

  39. Park, S.I., Hodgins, J.K.: Data-driven modeling of skin and muscle deformation. In: ACM SIGGRAPH 2008 papers, pp. 1–6 (2008)

  40. Saßenroth, D., Meyer, A., Salewsky, B., Kroh, M., Norman, K., Steinhagen-Thiessen, E., Demuth, I.: Sports and exercise at different ages and leukocyte telomere length in later life–data from the Berlin aging study II (BASE-II). PloS one 10(12), e0142131 (2015)

    Google Scholar 

  41. Shahri, M.M., Jahromi, A.E., Houshmand, M.: An integrated fuzzy inference system and AHP approach for criticality analysis of assets: a case study of a gas refinery. J. Intell. Fuzzy Syst. 41(1), 199–217 (2021)

    Google Scholar 

  42. Shanmugavadivu, P., Balasubramanian, K., Muruganandam, A.: Particle swarm optimized bi-histogram equalization for contrast enhancement and brightness preservation of images. Vis. Comput. 30(4), 387–399 (2014)

    Google Scholar 

  43. Shen, Q., Jensen, R.: Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring. Pattern Recogn. 37(7), 1351–1363 (2004)

    MATH  Google Scholar 

  44. Singh, S., Mitra, D., Baghel, R.K.: Wireless powered communication network optimization using PSO-CS algorithm. Wirel. Netw. 27(6), 4151–4167 (2021)

    Google Scholar 

  45. Smith, C.J., Havenith, G.: Body mapping of sweating patterns in male athletes in mild exercise-induced hyperthermia. Eur. J. Appl. Physiol. 111(7), 1391–1404 (2011)

    Google Scholar 

  46. Terliksiz, S., Kalaoğlu, F., Eryürük, S.H.: Analysis of thermal comfort properties of jacquard knitted mattress ticking fabrics. Int. J. Cloth. Sci. Technol. 28(1), 105–114 (2016)

    Google Scholar 

  47. Uren, N., Okur, A.: Analysis and improvement of tactile comfort and low-stress mechanical properties of denim fabrics. Text. Res. J. 89(23–24), 4842–4857 (2019)

    Google Scholar 

  48. Wang, L., Garg, H., Li, N.: Pythagorean fuzzy interactive Hamacher power aggregation operators for assessment of express service quality with entropy weight. Soft. Comput. 25(2), 973–993 (2021)

    MATH  Google Scholar 

  49. Xi, C.: A study on the effectiveness of competitive core strength training on the throwing ability of men of different ages. Youth Sport 7, 45–47 (2016)

    Google Scholar 

  50. Xu, G., An, Q., Yang, J., et al.: Evaluation and its application of an improved PMV-PPD model based on individual differences. J. Xi’an Univ. Sci. Technol. 41(1), 55–61 (2021)

    Google Scholar 

  51. Yang, Y., Yu, X., Wang, X., Sun, Y., Zhang, P., Liu, X.: Effect of jade nanoparticle content and twist of cool-touch polyester filaments on comfort performance of knitted fabrics. Text. Res. J. 90(21–22), 2385–2398 (2020)

    Google Scholar 

  52. Zamporri, J., Aguinaldo, A.: The effects of a compression garment on lower body kinematics and kinetics during a drop vertical jump in female collegiate athletes. Orthop. J. Sports Med. 6(8), 1 (2018)

    Google Scholar 

  53. Zhang, Y., Zhao, J., Li, L.: Analysis of thermal comfort of different age groups in summer based on dissipation rate. J. Donghua Univ. (Natural Science) 42(02), 268–272298 (2016)

    Google Scholar 

  54. Zhou, S.M., Gan, J.Q.: Constructing accurate and parsimonious fuzzy models with distinguishable fuzzy sets based on an entropy measure. Fuzzy Sets Syst. 157(8), 1057–1074 (2006)

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This paper was financially supported by China Scholarship Council and Fujian Province Social Science Planning Project (FJ2020C049), national key research and development plan "science and technology in Winter Olympic Games" (2019YFF0302100) and International Cooperation Fund of Science and Technology Commission of Shanghai Municipality (21130750100).

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Correspondence to Pengpeng Cheng.

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Cheng, P., Wang, J., Zeng, X. et al. Research on winter sportswear comfort and its visual model. Vis Comput 39, 4371–4389 (2023). https://doi.org/10.1007/s00371-022-02596-x

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