Correlation Among Joint Motions Allows Classification of Parkinsonian Versus Normal 3-D Reaching

In this paper, an objective assessment for determining whether a person has Parkinson disease is proposed. This is achieved by analyzing the correlation between joint movements, since Parkinsonian patients often have trouble coordinating different joints in a movement. Thus, the auto-correlation coefficient of single joint movements and the cross-correlation between movements in a pair of joints (hand, wrist, elbow, and shoulder) were studied. These features were used to train and provide classification of subjects as having or not having Parkinson´s disease using the least square support vector machine (LS-SVM). Experimental results showed that using either auto-correlation or cross-correlation features for classification provided over 91% correct classification. Using both features together provided better performance (96.0%) than using either feature alone. In addition, the performance of LS-SVM is better than that of self-organizing map (SOM) and k-nearest neighbor (KNN) in this case.