Detection of Parkinson gait pattern based on vertical ground reaction force

This paper aims to identify gait patterns between normal healthy adults and PD patients based on three vertical ground reaction force gait features namely Maximum vertical heel contact (F_Z1), Vertical minimum midstance force (F_Z2) and F_Z3 known as Maximum vertical push off force. Based on the gait data extracted, it was found that all three vertical GRF features are higher for normal subjects as compared to PD group. Conversely, longer time is taken by PD group to complete a stance phase. Moreover, based on Analysis of Variance, it was also confirmed that all three vertical GRF features are statistical significant. Further, to verify the effectiveness of these features in discriminating anomalous PD gait pattern, Support Vector Machine and Naive Bayes classifier are used as classifiers. Initial results showed that SVM with RBF kernel outshine other classifiers with 96.9% accuracy, 100% sensitivity and 95% specificity.