Logo PTI Logo FedCSIS

Position Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 36

Assessing the Accuracy of Body Measurements through Regression Analysis

, , , , ,

DOI: http://dx.doi.org/10.15439/2023F6427

Citation: Position Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 36, pages 35–41 ()

Full text

Abstract. The digitalization of individual garment pattern construction presents challenges in accurately obtaining body measurements and constructing patterns tailored to specific individuals. This paper addresses the technological and conceptual aspects of transitioning from traditional, in-person tailoring to remote, digital pattern creation. It explores the need for algorithms that describe pattern construction operations in a computationally executable manner and the reliance on selfmeasurements by clients or their trusted individuals. The study focuses on evaluating the reliability of self-measurements and the potential errors introduced in the pattern construction process. The paper proposes the use of regression analysis to identify suspicious or erroneous measurement sets and assess their impact on the resulting garment shape. The study investigates the hypotheses regarding the identification of incorrect measurements through regression analysis and the application of publicly available artificial intelligence solutions. The findings contribute to enhancing the precision and reliability of digital individual garment pattern construction, facilitating remote creation and production processes.

References

  1. Ruiz N., Bueno M.B., Bolkart T., Arora, Lin M., Romero J., Bala R. Human body measurement estimation with adversarial augmentation. International Conference on 3D Vision, 2022 https://www.amazon.science/publications/human-body-measurement-estimation-with-adversarial-augmentation (Accessed 23.05.2023).
  2. Roknabadi A.D., Latifi M., Saharkhiz S., Aboltakhty H. Human body measurement system in clothing using image processing. World Applied Sciences Journal 19(1):112-119 http://dx.doi.org/10.5829/idosi.wasj.2012.19.01.1306, January 2012.
  3. Liu X., Wu Y., Wu H. Machine Learning Enabled 3D Body Measurement Estimation Using Hybrid Feature Selection and Bayesian Search. Appl. Sci. 2022, 12(14), 7253; https://doi.org/10.3390/app12147253.
  4. Ashmawi S., Alharbi M., Almaghrabi A., Alhothali A. Fitme: Body Measurement Estimations using Machine Learning Method. Procedia Computer Science. Volume 163, Pages 209-217, 2019. https://doi.org/10.1016/j.procs.2019.12.102.
  5. Bye E., Labat K.L., Delong M. R. Analysis of Body Measurement Systems for Apparel. Clothing and Textiles Research Journal 24(2):66-79, March 2006 http://dx.doi.org/10.1177/0887302X0602400202
  6. https://ieee-dataport.org/open-access/dataset-ieee-ic-3dbp-comparative-analysis-anthropometric-methods.
  7. Bartol K., Bojanić D., Petković T., Pribanic T. A Review of Body Measurement Using 3D Scanning, IEEE Access, http://dx.doi.org/10.1109/ACCESS.2021.3076595, 2021.
  8. Lu J., Wang M.J. Automated anthropometric data collection using 3D whole body scanners, DBPL, Expert Systems with Applications 35(1-2):407-414, July 2008. http://dx.doi.org/10.1016/j.eswa.2007.07.008
  9. Kuribayashi M., Nakai K., Funabiki N. Image-Based Virtual Try-on System With Clothing-Size Adjustment. http://dx.doi.org/10.48550/arXiv.2302.14197, 2023.
  10. Pleuss J.D., Talty K., Morse S., Kuiper P., Scioletti M., Heymsfield S.B., Thomas D.M. A machine learning approach relating 3D body scans to body composition in humans. Eur J Clin Nutr. 2019 Feb; 73(2): 200–208, published online 2018 Oct 12. http://dx.doi.org/10.1038/s41430-018-0337-1
  11. Bartol, K., Bojanić, D., Petković, T., Peharec, S., Pribanić, T. Linear Regression vs. Deep Learning:A Simple Yet Effective Baseline for Human Body Measurement. Sensors, 22, 1885, 2022. https://doi.org/10.3390/s22051885
  12. Kus A., Unver E., Taylor A. A Comparative Study of 3D Scanning in Engineering, Product and Transport Design and Fashion Design Education. Computer Applications in Engineering Education 17(3):263 – 271, September 2009http://dx.doi.org/10.1002/cae.20213
  13. Seifert, E., Griffin, L. Comparison and Validation of Traditional and 3D Scanning Anthropometric Methods to Measure the Hand. Paper presented at 11th Int. Conference and Exhibition on 3D Body Scanning and Processing Technologies. https://doi.org/10.15221/20.41 , 2020.
  14. Skorvankova, D., Riečický, A., Madaras, M. Automatic Estimation of Anthropometric Human Body Measurements. 17th International Conference on Computer Vision Theory and Applications. (2021) http://dx.doi.org/10.5220/0010878100003124, https://www.scitepress.org/PublishedPapers/2022/108781/108781.pdf
  15. Rumbo-Rodríguez L, Sánchez-SanSegundo M, Ferrer-Cascales R, García-D'Urso N, Hurtado-Sánchez JA, Zaragoza-Martí A. Comparison of Body Scanner and Manual Anthropometric Measurements of Body Shape: A Systematic Review. Int J Environ Res Public Health. 2021 Jun 8;18(12):6213. http://dx.doi.org/10.3390/ijerph18126213
  16. ChatGPT May/, 12 Version, https://help.openai.com/en/articles/6825453-chatgpt-release-notes
  17. OpenAI. https://chat.openai.com