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An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy

Ankle 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-i...

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Autores principales: Choisne, Julie, Fourrier, Nicolas, Handsfield, Geoffrey, Signal, Nada, Taylor, Denise, Wilson, Nichola, Stott, Susan, Besier, Thor F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7290444/
https://www.ncbi.nlm.nih.gov/pubmed/32408489
http://dx.doi.org/10.3390/jcm9051432
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author Choisne, Julie
Fourrier, Nicolas
Handsfield, Geoffrey
Signal, Nada
Taylor, Denise
Wilson, Nichola
Stott, Susan
Besier, Thor F.
author_facet Choisne, Julie
Fourrier, Nicolas
Handsfield, Geoffrey
Signal, Nada
Taylor, Denise
Wilson, Nichola
Stott, Susan
Besier, Thor F.
author_sort Choisne, Julie
collection PubMed
description Ankle 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.
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spelling pubmed-72904442020-06-15 An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy Choisne, Julie Fourrier, Nicolas Handsfield, Geoffrey Signal, Nada Taylor, Denise Wilson, Nichola Stott, Susan Besier, Thor F. J Clin Med Article Ankle 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. MDPI 2020-05-12 /pmc/articles/PMC7290444/ /pubmed/32408489 http://dx.doi.org/10.3390/jcm9051432 Text en © 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/).
spellingShingle Article
Choisne, Julie
Fourrier, Nicolas
Handsfield, Geoffrey
Signal, Nada
Taylor, Denise
Wilson, Nichola
Stott, Susan
Besier, Thor F.
An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy
title An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy
title_full An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy
title_fullStr An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy
title_full_unstemmed An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy
title_short An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy
title_sort unsupervised data-driven model to classify gait patterns in children with cerebral palsy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7290444/
https://www.ncbi.nlm.nih.gov/pubmed/32408489
http://dx.doi.org/10.3390/jcm9051432
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