Open AccessVertical ground reaction force marker for Parkinson’s disease
Author(s) -
Nafiul Alam,
Amanmeet Garg,
Tamanna T. K. Munia,
Reza Fazel-Rezai,
Kouhyar Tavakolian
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0175951
Subject(s) - support vector machine , random forest , artificial intelligence , computer science , gait , gait analysis , pattern recognition (psychology) , feature selection , center of pressure (fluid mechanics) , ground reaction force , stride , machine learning , radial basis function kernel , parkinson's disease , physical medicine and rehabilitation , medicine , disease , kernel method , engineering , pathology , physics , computer security , kinematics , aerodynamics , classical mechanics , aerospace engineering
Parkinson’s disease (PD) patients regularly exhibit abnormal gait patterns. Automated differentiation of abnormal gait from normal gait can serve as a potential tool for early diagnosis as well as monitoring the effect of PD treatment. The aim of current study is to differentiate PD patients from healthy controls, on the basis of features derived from plantar vertical ground reaction force (VGRF) data during walking at normal pace. The current work presents a comprehensive study highlighting the efficacy of different machine learning classifiers towards devising an accurate prediction system. Selection of meaningful feature based on sequential forward feature selection, the swing time, stride time variability, and center of pressure features facilitated successful classification of control and PD gaits. Support Vector Machine (SVM), K-nearest neighbor (KNN), random forest, and decision trees classifiers were used to build the prediction model. We found that SVM with cubic kernel outperformed other classifiers with an accuracy of 93.6%, the sensitivity of 93.1%, and specificity of 94.1%. In comparison to other studies, utilizing same dataset, our designed prediction system improved the classification performance by approximately 10%. The results of the current study underscore the ability of the VGRF data obtained non-invasively from wearable devices, in combination with a SVM classifier trained on meticulously selected features, as a tool for diagnosis of PD and monitoring effectiveness of therapy post pathology.