Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

ABSTRACT:

Background

This study aimed to improve the automatic probabilistic classification of joint motion gait patterns in children with cerebral palsy by using the expert knowledge available via a recently developed Delphi-consensus study. To this end, this study applied both Naïve Bayes and Logistic Regression classification with varying degrees of usage of the expert knowledge (expert-defined and discretized features). A database of 356 patients and 1719 gait trials was used to validate the classification performance of eleven joint motions.

Hypotheses

Two main hypotheses stated that: (1) Joint motion patterns in children with CP, obtained through a Delphi-consensus study, can be automatically classified following a probabilistic approach, with an accuracy similar to clinical exp

SUBMITTER: De Laet T 

PROVIDER: S-EPMC5453476 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature