Classification of Parkinson gait and normal gait using Spatial-Temporal Image of Plantar pressure

The purpose of this paper is the classification of Spatial-Temporal Image of Plantar pressure (STIP) among normal step and the patients step of Parkinson disease. For this, we created a new image data, STIP, that have information of the change of plantar pressure during heel to toe motion (i.e., contain spatial and temporal information for plantar pressure). To get STIP, the walking of 21 patients with Parkinson disease and 17 age-matched healthy subjects were recorded and analyzed using in-shoe dynamic pressure measuring system with comfort walking. For feature extraction of gait, we applied Principal component analysis (PCA) to STIP and calculated weights of STIP on each principal components. Then, we build hard margin Support Vector Machine (SVM) classifier for gait recognition and test of generalization performance using normalized weights on PCs of STIP. SVM result indicated an overall accuracy of 91.73% by the RBF(Radial Basis Function) kernel function. These results demonstrate considerable potential in applying SVMs in gait classification for many applications.