Parkinson's disease classification using gait analysis via deterministic learning.
Gait analysis plays an important role in maintaining the well-being of human mobility and health care, and is a valuable tool for obtaining quantitative information on motor deficits in Parkinson's disease (PD). In this paper, we propose a method to classify (diagnose) patients with PD and healthy control subjects using gait analysis via deterministic learning theory. The classification approach consists of two phases: a training phase and a classification phase. In the training phase, gait characteristics represented by the gait dynamics are derived from the vertical ground reaction forces under the usual and self-selected paces of the subjects. The gait dynamics underlying gait patterns of healthy controls and PD patients are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. The gait patterns of healthy controls and PD patients constitute a training set. In the classification phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern of a certain PD patient to be classified (diagnosed), a set of classification errors are generated. The average L1norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test PD gait pattern according to the smallest error principle. When the gait patterns of 93 PD patients and 73 healthy controls are classified with five-fold cross-validation method, the accuracy, sensitivity and specificity of the results are 96.39%, 96.77% and 95.89%, respectively. Based on the results, it may be claimed that the features and the classifiers used in the present study could effectively separate the gait patterns between the groups of PD patients and healthy controls.
School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China. Electronic address: zw0597@126.com.
Wei
W
Zeng
School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China.
Fenglin
F
Liu
School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China.
Qinghui
Q
Wang
School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China.
Ying
Y
Wang
Department of Orthopaedic Surgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China.
Limin
L
Ma
Department of Orthopaedic Surgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China.
Yu
Y
Zhang
D016022:Case-Control Studies; D016208:Databases, Factual; D005684:Gait; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer; D010300:Parkinson Disease / Q000145:classification* / Q000175:diagnosis / Q000503:physiopathology
Deterministic learning; Gait analysis; Ground reaction force; Movement disorders; Parkinson's disease (PD)