Prediction of Military Combat Clothing Size Using Decision Trees and 3D Body Scan Data

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aut.filerelease.date2023-09-25
aut.relation.articlenumber103435en_NZ
aut.relation.endpage103435
aut.relation.journalApplied Ergonomicsen_NZ
aut.relation.startpage103435
aut.relation.volume95en_NZ
aut.researcherHume, Patria
dc.contributor.authorKolose, Sen_NZ
dc.contributor.authorStewart, Ten_NZ
dc.contributor.authorHume, Pen_NZ
dc.contributor.authorTomkinson, GRen_NZ
dc.date.accessioned2021-05-25T02:35:48Z
dc.date.available2021-05-25T02:35:48Z
dc.date.copyright2021en_NZ
dc.date.issued2021en_NZ
dc.description.abstractAim To determine how well decision tree models can predict tailor-assigned uniform sizes using anthropometry data from the New Zealand Defence Force Anthropometry Survey (NZDFAS). This information may inform automatic sizing systems for military personnel. Methods Anthropometric data from two separate samples of the New Zealand Defence Force military were used. Data on Army personnel from the NZDFAS (n = 583) were used to develop a series of shirt- and trouser-size prediction models based on decision trees. Different combinations of physical, automatic, and post-processed measurements (the latter two derived from a 3D body scan) were trialled, and the models with the highest cross-validation accuracy were retained. The accuracy of these models were then tested on an independent sample of Army recruits (n = 154). Results The automated measurement method (measurements derived automatically by the body scanner software) were the best predictors of shirt size (58.1% accuracy) and trouser size (61.7%), with body weight and waist girth being the strongest predictors. Clothing sizes that were incorrectly predicted by the model where generally one size above or below the tailor-predicted size. Conclusions Anthropometry measurements, when used with decision tree models, show promise for classifying clothing size. Methodological changes such as fitting gender-specific models, using additional anthropometry variables, and testing other data mining techniques are avenues for future work. More research is required before fully automated body scanning is a viable option for obtaining fast and accurate clothing sizes for military clothing and logistics departments.
dc.identifier.citationApplied Ergonomics, 95, 103435.
dc.identifier.doi10.1016/j.apergo.2021.103435en_NZ
dc.identifier.issn0003-6870en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14213
dc.languageenen_NZ
dc.publisherElsevier BVen_NZ
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S000368702100082X?via%3Dihub
dc.rightsCopyright © 2021 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectAnthropometry; 3-D body scanning; Decision tree; CART; Clothing size
dc.titlePrediction of Military Combat Clothing Size Using Decision Trees and 3D Body Scan Dataen_NZ
dc.typeJournal Article
pubs.elements-id430006
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Health & Environmental Science
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation/Sports Performance Research Institute New Zealand
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation/Sports Performance Research Institute New Zealand/Sports Kinesiology Injury Prevention & Performance Research Group
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HS Sports & Recreation 2018 PBRF
pubs.organisational-data/AUT/PVC - Research & Innovation
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