Identification of Parkinson's disease by using multichannel Vertical Ground Reaction Force signals

In this paper, we analyze Vertical Ground Reaction Force (VGRF) signals recorded from normal subjects as well as from subjects attained with Parkinson's disease (PD). The aim of this study is to identify abnormal gait patterns in order to detect patients who are potentially attained with PD. This is done by extracting various significant features from sensors located at 16 different positions on the right and left foot. Finally, extracted features are used to classify between health control and PD subjects and predict the Parkinson phase. Results have shown that extracting parameters based on the summation of sensors output of each foot may hide the conveyed information of VGRF signals. Moreover, frequency-related features are able to classify between PD subjects with different Hoehn and Yahr stages. Finally, results have revealed the importance of power distribution in VGRF signals that vary with PD stages.