An Unsupervised Data-driven Model to Classify Gait Patterns in Children With Cerebral Palsy

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aut.relation.articlenumber1432en_NZ
aut.relation.issue5en_NZ
aut.relation.journalJournal of Clinical Medicineen_NZ
aut.relation.volume9en_NZ
aut.researcherDrabsch, Julie
dc.contributor.authorChoisne, Jen_NZ
dc.contributor.authorFourrier, Nen_NZ
dc.contributor.authorHandsfield, Gen_NZ
dc.contributor.authorSignal, Nen_NZ
dc.contributor.authorTaylor, Den_NZ
dc.contributor.authorWilson, Nen_NZ
dc.contributor.authorStott, Sen_NZ
dc.contributor.authorBesier, TFen_NZ
dc.date.accessioned2020-07-02T22:48:35Z
dc.date.available2020-07-02T22:48:35Z
dc.date.copyright2020en_NZ
dc.date.issued2020en_NZ
dc.description.abstractAnkle and foot orthoses are commonly prescribed to children with cerebral palsy (CP). It is unclear whether 3D gait analysis (3DGA) provides sufficient and reliable information for clinicians to be consistent when prescribing orthoses. Data-driven modeling can probe such questions by revealing non-intuitive relationships between variables such as 3DGA parameters and gait outcomes of orthoses use. The purpose of this study was to (1) develop a data-driven model to classify children with CP according to their gait biomechanics and (2) identify relationships between orthotics types and gait patterns. 3DGA data were acquired from walking trials of 25 typically developed children and 98 children with CP with additional prescribed orthoses. An unsupervised self-organizing map followed by k-means clustering was developed to group different gait patterns based on children's 3DGA. Model inputs were gait variable scores (GVSs) extracted from the gait profile score, measuring root mean square differences from TD children's gait cycle. The model identified five pathological gait patterns with statistical differences in GVSs. Only 43% of children improved their gait pattern when wearing an orthosis. Orthotics prescriptions were variable even in children with similar gait patterns. This study suggests that quantitative data-driven approaches may provide more clarity and specificity to support orthotics prescription.en_NZ
dc.identifier.citationJournal of Clinical Medicine, 9(5), 1432.
dc.identifier.doi10.3390/jcm9051432en_NZ
dc.identifier.issn2077-0383en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/13487
dc.languageengen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/2077-0383/9/5/1432
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccessen_NZ
dc.subject3D gait analysisen_NZ
dc.subjectAnkle foot orthosisen_NZ
dc.subjectCerebral palsyen_NZ
dc.subjectData-driven modelen_NZ
dc.subjectGait variable scoreen_NZ
dc.titleAn Unsupervised Data-driven Model to Classify Gait Patterns in Children With Cerebral Palsyen_NZ
dc.typeJournal Article
pubs.elements-id375977
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Health & Environmental Science
pubs.organisational-data/AUT/Health & Environmental Science/Clinical Sciences
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/HH Clinical Sciences 2018 PBRF
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