Gait is usually a crucial indicator for recognizing and evaluating the progression of Parkinson's disease (PD). To assess gait variability in the elderly with PD in a continuous and natural way, we pay more attention to their feet pressure variability while walking, and try to obtain the varying patterns of center of pressure (CoP). In this paper, we propose a framework based on the bimodal distribution probability density functions (BDPDFs) to recognize gait patterns of PD patients. The result evaluated with the 10-fold cross-validation method demonstrates that the Bagging classifier is able to provide classification precision of 82.4% with AUC of 88.3%. The features and the classifiers used in the present study can also be used to study the effect of dopamine and rehabilitation in PD patients.
['Jiangbo Jia', 'Hongbo Ni', 'Tianben Wang', 'Weichao Zhao', 'Yalong Song', 'Junquan Deng', 'Xingshe Zhou']
Intensive analysis of gait in the elderly with Parkinson's disease using center of pressure during walking