期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2017
卷号：13
期号：5
页码：1
DOI：10.1177/1550147717707417
出版社：Hindawi Publishing Corporation
摘要：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, logistic regression, and neural network to classify the two groups of patients. The highest classification accuracy obtained by support vector machine classifier was 79.66%, with 0.8790 area under the curve. A 76.2% classification accuracy was obtained with 0.7832 area under the curve through logistic regression. A 83.10% classification accuracy was obtained by neural network classifier, with 0.889 area under the curve. Strong distinguishability of the models between the two groups directs the high possibility of motor impairment classification through biomechanical parameters in patients with Parkinson’s disease based on the clinical scale.
关键词：Slight–mild Parkinson’s disease and moderate–severe Parkinson’s disease; wearable inertial sensors; biomechanical parameters; Least Absolute Shrinkage and Selection Operator; machine learning; support vector machine; logistic regression; neural network
