Correlation among Joint Motions Allows Classification of Parkinsonian versus Normal 3D Reaching
Identifieur interne : 000235 ( Ncbi/Curation ); précédent : 000234; suivant : 000236Correlation among Joint Motions Allows Classification of Parkinsonian versus Normal 3D Reaching
Auteurs : Jacky Chan ; Howard Leung ; Howard PoiznerSource :
- IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society [ 1534-4320 ] ; 2009.
Abstract
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. 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.
Url:
DOI: 10.1109/TNSRE.2009.2023296
PubMed: 19497826
PubMed Central: 2888650
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