An Exploratory Study of Body Measurement Prediction Using Machine Learning and 3D Body Scans

Abstract

Obtaining accurate body measurements is a critical step when designing products to fit the human body. Compared to traditional manual methods, 3D body scanning has fundamentally enhanced the accessibility of the body, however, the datasets extracted from 3D body scans often have missing values. Recently, the applications of data-driven machine learning (ML) methods in anthropometrics studies and clothing-related work have been increasing. However, there has been limited research on exploring if missing data and difficult-to-extract measurements from 3D scans could be predicted accurately and efficiently by using ML methods. Therefore, this exploratory study investigates the potential use of four mainstream ML methods in improving the usefulness of a 3D body scan dataset.

Keywords

body measurement prediction 3D body scan machine learning

Get full access to this article

View all access options for this article.

References

Alemany

Remon

Ballester

Durá

J. V.

Nácher

Parrilla

González

J. C.

(2022). Data management and processing of 3D body scans. In Digital manufacturing technology for sustainable anthropometric apparel (pp. 97–116). Woodhead Publishing.

Ashdown

S. P.

(2020). Full body 3-D scanners. In Zakaria

Gupta

(Eds.), Anthropometry, apparel sizing and design (pp. 145–168). Woodhead Publishing.

Balach

Cichocka

Frydrych

Kinsella

(2020). Initial investigation into real 3D body scanning versus avatars for the virtual fitting of garments. Autex Research Journal, 20(2), 128–132. https://doi.org/10.2478/aut-2019-0037

Bhuyan

M. A. U. A.

Griffin

(2020, November). Make it easy: Reliability of automatic measurement for 3D hand scanning. In Proceedings of the 11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies. 38, Hometrica Consulting. https://doi.org/10.15221/20.38

Bishop

C. M.

(2006). Pattern recognition and machine learning (pp. 1–3). Springer New York. https://scholar.google.com/citationsview_op=view_citation&hl=en&user=gsr-K3ADUvAC&citation_for_view=gsr-K3ADUvAC:8k81kl-MbHgC

Bonin

Ackermann

Radke

Peters

Wischniewski

(2023). Anthropometric dataset for the German working-age population using 3D body scans from a regional epidemiological health study and a weighting algorithm. Ergonomics, 66(8), 1057–1071. https://doi.org/10.1080/00140139.2022.2130440

Bullas

Thelwell

Choppin

(2022). The future of 3D body scanning within ISAK? A natural home or a conflicting toolset. International Journal of Kinanthropometry, 2(S1), 2. https://doi.org/10.34256/ijk22S1

Casadei

Kiel

(2020). Anthropometric Measurement. Treasure Island, FL.

Cook

(2021, October 14). ‘Comfort equals confidence': Oregon professor helps Old Navy improve plus-size options for women. KGW8. https://www.kgw.com/article/life/oregon-professor-old-navy/283-d508cb1d-8d68-445f-b115-ac48d348a960

10.

Costa

Silva

Rocha

Maia

Vieira

(2018). Study on machine learning algorithms to automatically identifying body type for clothing model recommendation. In Rocha

Adeli

Reis

Costanzo

(Eds.), Trends and advances in information systems and technologies (pp. 74–78). Springer.

11.

Demirel

H. O.

Ahmed

Duffy

V. G.

(2022). Digital human modeling: A review and reappraisal of origins, present, and expected future methods for representing humans computationally. International Journal of Human–Computer Interaction, 38(10), 897–937. https://doi.org/10.1080/10447318.2021.1976507

12.

Geng

Zhang

(2020). Short-term wind speed prediction based on principal component analysis and LSTM. Applied Sciences, 10(13), 4416. https://doi.org/10.3390/app10134416

13.

Gibelli

Dolci

Cappella

Sforza

(2020). Reliability of optical devices for three-dimensional facial anatomy description: A systematic review and meta-analysis. International Journal of Oral and Maxillofacial Surgery, 49(8), 1092–1106. https://doi.org/10.1016/j.ijom.2019.10.019

14.

Gill

Ahmed

Parker

C. J.

Hayes

S. G.

(2017, October). Not all body scanning measurements are valid: Perspectives from pattern practice. In Proceedings of the 8th International Conference and Exhibition on 3D Body Scanning and Processing Technologies (pp. 43–52). Hometrica Consulting.

15.

Goodfellow

Bengio

Courville

(2016). Deep learning. MIT Press.

16.

Graham

J. W.

(2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576. https://doi.org/10.1146/annurev.psych.58.110405.085530

17.

Griffin

Juhnke

Pokorny

Seifert

Savvateev

(2023). Body–product relationship for aging women examined through the Lens of functional anthropometry. Ergonomics in Design, 31(3), 13–25. https://doi.org/10.1177/10648046221134567

18.

Hastie

Tibshirani

Friedman

(2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.

19.

Hobbs-Murphy

Morris

Park

(2022). A Case Study of Developing a Paralympic Shooting Jacket for Disabled Athletes. Clothing and Textiles Research Journal, 42(1), 51–68. https://doi.org/10.1177/0887302X221102920

20.

Human Solutions of North America, Inc. (2018). Human Solutions Body Measurements Guide. Unpublished internal company document.

21.

Idrees

Gill

Vignali

(2023). Mobile 3D body scanning applications: A review of contact-free AI body measuring solutions for apparel. Journal of The Textile Institute, 1–12. https://doi.org/10.1080/00405000.2023.2216099

22.

International Organization for Standardization. (2017). Basic human body measurement for technological design—Part 1: Body measurement definitions and landmarks (ISO Standard No. 7250-1:2017). https://www.iso.org/standard/65246.html

23.

Istook

C. L.

Hwang

S. J.

(2001). 3D Body scanning systems with application to the apparel industry. Journal of Fashion Marketing and Management: An International Journal, 5(2), 120–132. https://doi.org/10.1108/EUM0000000007283

24.

James

Witten

Hastie

Tibshirani

(2013). An introduction to statistical learning. Springer.

25.

Kim

(2019). Patternmaking for menswear: Classic to contemporary. Fairchild Books.

26.

Liu

Wang

Kamalha

Zeng

(2017a). Construction of a prediction model for body dimensions used in garment pattern making based on anthropometric data learning. The Journal of The Textile Institute, 108(12), 2107–2114. https://doi.org/10.1080/00405000.2017.1315794

27.

Liu

Zeng

Bruniaux

Wang

Kamalha

Tao

(2017b). Fit evaluation of virtual garment try-on by learning from digital pressure data. Knowledge-Based Systems, 133, 174–182. https://doi.org/10.1016/j.knosys.2017.07.007

28.

Liu

(2022). Machine Learning Enabled 3D Body Measurement Estimation Using Hybrid Feature Selection and Bayesian Search. Applied Sciences, 12(14), 7253. https://doi.org/10.3390/app12147253

29.

Zhu

Wan

Gao

(2021). Outfit compatibility prediction with multi-layered feature fusion network. Pattern Recognition Letters, 147, 150–156. https://doi.org/10.1016/j.patrec.2021.04.009

30.

Markiewicz

Witkowski

Sitnik

Mielicka

(2017). 3D Anthropometric algorithms for the estimation of measurements required for specialized garment design. Expert Systems with Applications, 85, 366–385. https://doi.org/10.1016/j.eswa.2017.04.052

31.

Meyer

Birregah

Beauseroy

Grall

Lauxerrois

(2023). Missing body measurements prediction in fashion industry: a comparative approach. Fashion and Textiles, 10(37). https://doi.org/10.1186/s40691-023-00357-5

32.

Miguel-Hurtado

Guest

Stevenage

S. V.

Neil

G. J.

Black

(2016). Comparing machine learning classifiers and linear/logistic regression to explore the relationship between hand dimensions and demographic characteristics. PLOS ONE, 11(11), e0165521. https://doi.org/10.1371/journal.pone.0165521

33.

Newman

D. A.

(2014). Missing data: Five practical guidelines. Organizational Research Methods, 17(4), 372–411. https://doi.org/10.1177/1094428114548590

34.

Pleuss

J. D.

Talty

Morse

Kuiper

Scioletti

Heymsfield

S. B.

Thomas

D. M.

(2019). A machine learning approach relating 3D body scans to body composition in humans. European Journal of Clinical Nutrition, 73(2), 200–208. https://doi.org/10.1038/s41430-018-0337-1

35.

Pokorny

Seifert

Griffin

Holschuh

Juhnke

Savvateev

(2019). Validation of the Artec Eva for hand anthropometric data collection. Proceedings of International Textile and Apparel Association Annual Conference, 76(1), https://doi.org/10.31274/itaa.8376

36.

Price

(2023, April 24). Our Favorite Pieces from Outdoor Afro x REI’s New Hiking Collection. https://www.travelandleisure.com/best-items-from-outdoor-afro-x-rei-6746921

37.

Ramos-Jiménez

Hernández-Torres

R. P.

Villalobos-Molina

Romero

R. U.

(2018). Plethysmographic and anthropometric validation of a 3D body image digitizer to determine body dimensions. International Journal of Industrial Ergonomics, 67, 1–5. https://doi.org/10.1016/j.ergon.2018.04.006

38.

Rativa

Fernandes

B. J.

Roque

(2018). Height and weight estimation from anthropometric measurements using machine learning regressions. IEEE Journal of Translational Engineering in Health and Medicine, 6, 1–9. https://doi.org/10.1109/JTEHM.2018.2797983

39.

Rumbo-Rodríguez

Sánchez-SanSegundo

Ferrer-Cascales

García-D’Urso

Hurtado-Sánchez

J. A.

Zaragoza-Martí

(2021). Comparison of body scanner and manual anthropometric measurements of body shape: A systematic review. International Journal of Environmental Research and Public Health, 18(12), 6213. https://doi.org/10.3390/ijerph18126213

40.

Seber

G. A.

Lee

A. J.

(2012). Linear regression analysis. John Wiley & Sons.

41.

Sobhiyeh

Dunkel

Dechenaud

Kennedy

Shepherd

Heymsfield

Wolenski

(2019). Crotch detection on 3D optical scans of human subjects. Electronic Imaging, 2019(16), 10–11. https://doi.org/10.2352/ISSN.2470-1173.2019.16.3DMP-010

42.

Vonk

T. E.

Daanen

H. A.

D’Apuzzo

(2015). Validity and repeatability of the sizestream 3D scanner and Poikos modeling system. In Proceedings of the 6th International Conference on 3D Body Scanning Technologies (pp. 293–299). Hometrica Consulting.