Author/Authors :
Xia, Yi School of Electrical Engineering and Automation - Anhui University - Hefei, China , Ye, Qiang Information Technology Research Centre - Nanjing Sport Institute - Nanjing, China , Gao, Qingwei School of Electrical Engineering and Automation - Anhui University - Hefei, China , Lu, Yixiang School of Electrical Engineering and Automation - Anhui University - Hefei, China , Zhang, Dexiang School of Electrical Engineering and Automation - Anhui University - Hefei, China
Abstract :
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 𝑝
value of less than 10−5, 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.
