Understanding Postural Response of Parkinson’s Subjects Using Nonlinear Dynamics and Support Vector Machines

Parkinson’s disease (PD) produces slowness, stiffness, tremor, shuffling gait and postural instability. This paper is to present an approach to classification and understanding of Parkinson’s patients using their postural response and analyzing it using a L2 norm metric in conjunction with support vector machines. Medication induced dyskinesia is a significant factor altering the postural response of Parkinson’s’ subjects. This paper demonstrates that classification accuracy can be significantly increased if dyskinesia and history of fall is carefully integrated in to the analysis. Long term goal of this research is toprovide an intelligent framework for automatic classification of the Parkinson’s’ subjects thus reducing the burden on clinical staff.