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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 1737953, 9 pages
Research Article

Symmetry Analysis of Gait between Left and Right Limb Using Cross-Fuzzy Entropy

1School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
2Information Technology Research Centre, Nanjing Sport Institute, Nanjing 210014, China

Received 16 October 2015; Accepted 24 January 2016

Academic Editor: Didier Delignières

Copyright © 2016 Yi Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The purpose of this paper is the investigation of gait symmetry problem by using cross-fuzzy entropy (C-FuzzyEn), which is a recently proposed cross entropy that has many merits as compared to the frequently used cross sample entropy (C-SampleEn). First, we used several simulation signals to test its performance regarding the relative consistency and dependence on data length. Second, the gait time series of the left and right stride interval were used to calculate the C-FuzzyEn values for gait symmetry analysis. Besides the statistical analysis, we also realized a support vector machine (SVM) classifier to perform the classification of normal and abnormal gaits. The gait dataset consists of 15 patients with Parkinson’s disease (PD) and 16 control (CO) subjects. The results show that the C-FuzzyEn values of the PD patients’ gait are significantly higher than that of the CO subjects with a value of less than , and the best classification performance evaluated by a leave-one-out (LOO) cross-validation method is an accuracy of 96.77%. Such encouraging results imply that the C-FuzzyEn-based gait symmetry measure appears as a suitable tool for analyzing abnormal gaits.