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

ABSTRACT: 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

SUBMITTER: Choisne J 

PROVIDER: S-EPMC7290444 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature