Research Repository

Gait classification in children with cerebral palsy by Bayesian approach

Zhang, Bai-ling and Zhang, Yanchun and Begg, Rezaul (2009) Gait classification in children with cerebral palsy by Bayesian approach. Pattern Recognition, 42 (4). pp. 581-586. ISSN 0031-3203

Full text for this resource is not available from the Research Repository.

Abstract

Cerebral palsy (CP) is generally considered as a nonprogressive neuro-developmental condition that occurs in early childhood and is associated with a motor impairment, usually affecting mobility and posture. Automatic accurate identification of cerebral palsy gait has many potential applications, for example, assistance in diagnosis, clinical decisionmaking and communication among the clinical professionals. In previous studies, support vector machine (SVM) and neural networks have been applied to classify CP gait patterns. However, one of the disadvantages of SVM and many neural network models is that given a gait sample, it only predicts a gait pattern class label without providing any estimate of the underlying probability, which is particularly important in Computer Aided Diagnostics applications. The objective of this study is to first investigate different pattern classification paradigms in the automatic gait analysis and address the significance of Bayesian classifier model, and then give a comprehensive performances comparison. Using a publicly available CP gait dataset (68 normal healthy and 88 with spastic diplegia form of CP), different features including the two basic temporal-spatial gait parameters (stride length and cadence) have been experimented. Various hold-out and crossvalidation testing show that the Bayesian model offers excellent classification performances compared with some popular classifiers such as random forest and multiple layer perceptron. With many advantages considered, Bayesian classifier model is very significant in establishing a clinical decision system for gait analysis.

Item Type: Article
Uncontrolled Keywords: ResPubID18309, gait classification, cerebral palsy, Bayesian approach
Subjects: FOR Classification > 0903 Biomedical Engineering
FOR Classification > 1106 Human Movement and Sports Science
SEO Classification > 9204 Public Health (excl. Specific Population Health)
Faculty/School/Research Centre/Department > School of Engineering and Science
Related URLs:
Depositing User: VUIR
Date Deposited: 27 Sep 2011 05:01
Last Modified: 27 Sep 2011 05:01
URI: http://vuir.vu.edu.au/id/eprint/4725
DOI: 10.1016/j.patcog.2008.09.025
ePrint Statistics: View download statistics for this item
Citations in Scopus: 14 - View on Scopus

Repository staff only

View Item View Item

Search Google Scholar