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

2020 ◽  
Vol 9 (5) ◽  
pp. 1432
Author(s):  
Julie Choisne ◽  
Nicolas Fourrier ◽  
Geoffrey Handsfield ◽  
Nada Signal ◽  
Denise Taylor ◽  
...  

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.

2014 ◽  
Vol 35 (5) ◽  
pp. 1137-1143 ◽  
Author(s):  
Luiz Alfredo Braun Ferreira ◽  
Veronica Cimolin ◽  
Pier Francesco Costici ◽  
Giorgio Albertini ◽  
Claudia Santos Oliveira ◽  
...  

Author(s):  
Firas Massaad ◽  
Frédéric Dierick ◽  
Adélaïde van den Hecke ◽  
Christine Detrembleur

2017 ◽  
Vol 55 ◽  
pp. 145-155 ◽  
Author(s):  
Andrea Ancillao ◽  
Marjolein M. van der Krogt ◽  
Annemieke I. Buizer ◽  
Melinda M. Witbreuk ◽  
Paolo Cappa ◽  
...  

2007 ◽  
Vol 25 (2) ◽  
pp. 157-165 ◽  
Author(s):  
Brigitte Toro ◽  
Christopher J. Nester ◽  
Pauline C. Farren

2010 ◽  
Vol 34 (2) ◽  
pp. 129-145 ◽  
Author(s):  
Emily Ridgewell ◽  
Fiona Dobson ◽  
Timothy Bach ◽  
Richard Baker

Studies which have examined the effects of ankle-foot orthoses (AFOs) on children with cerebral palsy (CP) often report insufficient detail about the participants, devices and testing protocols. The aim of this systematic review was to evaluate the level and quality of detail reported about these factors in order to generate best practice guidelines for reporting of future studies. A systematic search of the literature was conducted to identify studies which examined any outcome measure relating to AFO use in children with CP. A customized checklist was developed for data extraction and quality assessment. There was substantial variability in the level and quality of detail reported across the 41-paper yield. Many papers reported insufficient detail to allow synthesis of outcomes across studies. The findings of this review have been used to generate guidelines for best practice of reporting for AFO intervention studies. It is important to ensure homogeneity of gait pattern in a subject sample or to subdivide a sample to investigate the possibility that heterogeneity affected results. It is also important to describe the orthosis in sufficient detail that the device can be accurately replicated because differences in designs have been shown to affect outcomes. These guidelines will help researchers provide more systematic and detailed reports and thereby permit future reviewers to more accurately assess both the reporting and quality of orthotic interventions, and will facilitate synthesis of literature to enhance the evidence base.


2015 ◽  
Vol 42 (2) ◽  
pp. 133-137 ◽  
Author(s):  
Helle Mätzke Rasmussen ◽  
Dennis Brandborg Nielsen ◽  
Niels Wisbech Pedersen ◽  
Søren Overgaard ◽  
Anders Holsgaard-Larsen

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