Statistical analysis of parkinson disease gait classification using Artificial Neural Network

Abstract

The aim of this study is to investigate the parameters that could be used to identify abnormal gait pattern in Parkinson's disease subjects during normal walking. Hence, three types of gait parameters namely basic, kinematic and kinetic are evaluated. Initial findings showed that the average mean of cadence, step length and walking speed for Parkinson's disease patients are lower than normal subjects, while the mean of stride time for Parkinson's disease patients are higher. Further, for kinematic parameter, overall joint angle of hip, knee and ankle mean values are lower for Parkinson's disease patients as compared to normal group. In addition, for kinetic parameter, all mean values of ground reaction force parameters are higher for normal subjects with walking speed contributed as the major determinant. To evaluate the significant features that could be used as identification between PD and normal subjects, statistical analysis is conducted. Hence, based on the statistical analysis results, it was found that step length, walking speed, knee angle as well as vertical parameter of ground reaction force are the four significant features as indicators for classification of subject with Parkinson's disease based on the accuracy attained with Artificial Neural Network as classifier.