On Assessing Motor Disorders in Parkinson's Disease

In this paper we propose an automated method for assessing motor symptoms in Parkinson’s disease. Levodopa-induced dyskinesia (LID) and Freezing of Gait (FoG) are detected based on the analysis of signals recorded from wearable devices, i.e. accelerometers and gyroscopes, which are placed on certain positions on the patient’s body. The signals are initially pre-processed and then analyzed, using a moving window, in order to extract features from them. These features are used for LID and FoG assessment. Two classification techniques are employed, decision trees and random forests. The method has been evaluated using a group of patients and the obtained results indicate high classification ability, being 96.11% classification accuracy for FoG detection and 92.59% for LID severity assessment.