Literature DB >> 23948331

Categorization of gait patterns in adults with cerebral palsy: a clustering approach.

Nicolas Roche1, Didier Pradon, Julie Cosson, Johanna Robertson, Claire Marchiori, Raphael Zory.   

Abstract

Gait patterns in adults with cerebral palsy have, to our knowledge, never been assessed. This contrasts with the large number of studies which have attempted to categorize gait patterns in children with cerebral palsy. Several methodological approaches have been developed to objectively classify gait patterns in patients with central nervous system lesions. These methods enable the identification of groups of patients with common underlying clinical problems. One method is cluster analysis, a multivariate statistical method which is used to classify an entire data set into homogeneous groups or "clusters". The aim of this study was to determine, using cluster analysis, the principal gait patterns which can be found in adults with cerebral palsy. Data from 3D motion analyses of 44 adults with cerebral palsy were included. A hierarchical cluster analysis was used to subgroup the different gait patterns based on spatiotemporal and kinematic parameters in the sagittal and frontal planes. Five clusters were identified (C1-C5) among which, 3 subgroups were determined, based on spontaneous gait speed (C1/C2: slow, C3/C4: moderate and C5: almost normal). The different clusters were related to specific kinematic parameters that can be assessed in routine clinical practice. These 5 classifications can be used to follow changes in gait patterns throughout growth and aging as well to assess the effects of different treatments (physiotherapy, surgery, botulinum toxin, etc.) on gait patterns in adults with cerebral palsy.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  3D gait analysis; Adult; Cerebral palsy; Cluster analysis; Gait pattern

Mesh:

Year:  2013        PMID: 23948331     DOI: 10.1016/j.gaitpost.2013.07.110

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  6 in total

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Authors:  Rasmus F Frisk; Peter Jensen; Henrik Kirk; Laurent J Bouyer; Jakob Lorentzen; Jens B Nielsen
Journal:  J Neurophysiol       Date:  2017-09-13       Impact factor: 2.714

2.  Gait Patterns in Patients with Hereditary Spastic Paraparesis.

Authors:  Mariano Serrao; Martina Rinaldi; Alberto Ranavolo; Francesco Lacquaniti; Giovanni Martino; Luca Leonardi; Carmela Conte; Tiwana Varrecchia; Francesco Draicchio; Gianluca Coppola; Carlo Casali; Francesco Pierelli
Journal:  PLoS One       Date:  2016-10-12       Impact factor: 3.240

3.  Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions.

Authors:  Angkoon Phinyomark; Giovanni Petri; Esther Ibáñez-Marcelo; Sean T Osis; Reed Ferber
Journal:  J Med Biol Eng       Date:  2017-07-17       Impact factor: 1.553

4.  Exploring the Application of Pattern Recognition and Machine Learning for Identifying Movement Phenotypes During Deep Squat and Hurdle Step Movements.

Authors:  Sarah M Remedios; Daniel P Armstrong; Ryan B Graham; Steven L Fischer
Journal:  Front Bioeng Biotechnol       Date:  2020-04-29

5.  Gait patterns in hemiplegic patients with equinus foot deformity.

Authors:  M Manca; G Ferraresi; M Cosma; L Cavazzuti; M Morelli; M G Benedetti
Journal:  Biomed Res Int       Date:  2014-04-22       Impact factor: 3.411

6.  Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder.

Authors:  Masahiko Mukaino; Kei Ohtsuka; Hiroki Tanikawa; Fumihiro Matsuda; Junya Yamada; Norihide Itoh; Eiichi Saitoh
Journal:  J Vis Exp       Date:  2018-03-04       Impact factor: 1.355

  6 in total

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