Gait classification in children with cerebral palsy by Bayesian approach

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Zhang, Bai-ling, 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

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.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/4725
DOI https://doi.org/10.1016/j.patcog.2008.09.025
Official URL http://www.sciencedirect.com/science/article/pii/S...
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 1106 Human Movement and Sports Science
Historical > SEO Classification > 9204 Public Health (excl. Specific Population Health)
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID18309, gait classification, cerebral palsy, Bayesian approach
Citations in Scopus 33 - View on Scopus
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