Evaluating machine learning models for clothing size prediction using anthropometric measurements from 3D body scanning.

Ruqey Alhassawi, Simeon Gill, Steven Hayes, Kristina Brubacher

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Ruqey AlhassawiDepartment of Materials, The University of Manchester, Manchester, UK. ruqey.alhassawi@postgrad.manchester.ac.uk.

Simeon GillDepartment of Materials, The University of Manchester, Manchester, UK.

Steven HayesDepartment of Materials, The University of Manchester, Manchester, UK.

Kristina BrubacherDepartment of Materials, The University of Manchester, Manchester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An analysis of a dataset comprising 677 participants revealed substantial discrepancies in size categorization. Only 63 individuals (9.15%) maintained consistency across bust, waist, and hip measurements, whereas 614 participants (90.84%) exhibited size variations, and 35.45% were not adequately accommodated by the existing sizing scheme. These findings highlight significant challenges in garment selection, potentially leading to dissatisfaction and increased return rates. This study evaluated the effectiveness of support vector machine (SVM) and principal component analysis-SVM (PCA-SVM) models for clothing size prediction via 3D body scanning data. The traditional SVM model, which focuses on primary measurements, achieves an accuracy of 89.66%, outperforming the PCA-SVM model (68.97%), which incorporates additional dimensions. These results underscore the effectiveness of SVMs in predicting clothing size categories and emphasise the intricate relationship between body morphology and garment fit.

Indexed as

AnthropometryClothingImaging, Three-DimensionalMachine LearningAdultFemaleHumansMalePrincipal Component AnalysisSupport Vector MachineYoung Adult3D body scanBody measurementsClothing size predictionMachine learningStandard dress form

Identifiers

PMID41258093

DOI10.1038/s41598-025-24584-6

PMCPMC12630603

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.