Biomechanical parameter assessment for classification of Parkinson’s disease on clinical scale:

The primary goal of this study was to investigate computerized assessment methods to classify motor dysfunctioning of patients with Parkinson’s disease on the clinical scale. In this proposed system, machine learning–based computerized assessment methods were introduced to assess the motor performance of patients with Parkinson’s disease. Biomechanical parameters were acquired from six exercises through wearable inertial sensors: SensFoot V2 and SensHand V1. All patients were evaluated via neurologist by means of the clinical scale. The average rating was calculated from all exercise ratings given by clinicians to estimate overall rating for each patient. Patients were divided in two groups: slight–mild patients with Parkinson’s disease and moderate–severe patients with Parkinson’s disease according to average rating (“0: slight and mild” and “1: moderate and severe”). Feature selection methods were used for the selection of significant features. Selected features were trained in support vector machine, l...