Cluster Size Prediction for Military Clothing Using 3D Body Scan Data

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aut.filerelease.date2023-06-07
aut.relation.articlenumber103487en_NZ
aut.relation.endpage103487
aut.relation.journalApplied Ergonomicsen_NZ
aut.relation.startpage103487
aut.relation.volume96en_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.accessioned2021-06-14T01:19:44Z
dc.date.available2021-06-14T01:19:44Z
dc.date.copyright2021en_NZ
dc.date.issued2021en_NZ
dc.description.abstractAim To determine how anthropometric characteristics cluster in the New Zealand Defence Force, and to describe the characteristics of each cluster. This information can inform the development of new uniform sizing systems for the New Zealand Defence Force. Methods Anthropometric data (n = 84 variables) from 1,003 participants (212 females; 791 males) in the New Zealand Defence Force Anthropometry Survey (NZDFAS) were used. The dataset was stratified by gender and variables isolated based on their relevance to shirt and trouser sizing. Principal Component Analysis was used to identify the most important variables for clustering. A combination of two-step and k-means clustering was used to derive cluster characteristics. Results The PCA identified optimal clothing (shirt = body height and waist girth; and trouser = inseam length and hip girth for females; inseam length and waist girth for males) variables. Two-step and k-means clustering identified optimal cluster numbers of 6 and 10 for female and male clothing, respectively. The female clothing clusters were more variable (intra-cluster) and further apart (inter-cluster) compared to males. Conclusions Anthropometric measurements in combination with clustering techniques show promise for partitioning individuals into distinct groups. The anthropometry dimensions associated with each cluster can be used by the garment industry to develop specific sizing systems for the New Zealand Defence Force population.
dc.identifier.citationApplied Ergonomics, 96, 103487.
dc.identifier.doi10.1016/j.apergo.2021.103487en_NZ
dc.identifier.issn0003-6870en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14264
dc.languageenen_NZ
dc.publisherElsevier BVen_NZ
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0003687021001344
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; PCA; Cluster analysis; Clothing size; New Zealand Defence force
dc.titleCluster Size Prediction for Military Clothing Using 3D Body Scan Dataen_NZ
dc.typeJournal Article
pubs.elements-id431389
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/Physical Activity, Nutrition & the Outdoors Department
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/Human Potential Research Group
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
pubs.organisational-data/AUT/zAcademic Progression
pubs.organisational-data/AUT/zAcademic Progression/AP - Test
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