A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease

Abstract

BackgroundAssessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity.MethodsIn this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution.ResultsConcerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy).ConclusionsThe results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

“…For example, Hu et al [4] provide a vision-based solution for the Freezing of Gait (FoG) detection. Similarly, [5], [6] and [8] are gait based assessment solutions for Parkinson Disease (PD), cerebral palsy and variety of chronic diseases progression, respectively. Gait analysis requires data acquisition and extraction tools of the gait features.…”

An artificial neural network approach to detect presence and severity of Parkinson’s disease via gait parameters

“…This procedure allowed us to focus on the most discriminative parameters, irrespective of the gait speed. Previous studies on gait deficit classification [ 28 , 31 , 34 ] did not control for gait speed when comparing patients with healthy subjects, and thus, may have given importance to some speed-related classifiers. However, when classifying the gait deficit of PwPD according to the H-Y staging system, we could not control for gait speed.…”

“…However, few studies have attempted to identify and classify gait deficits using machine-learning approaches in neurological disorders, including Huntington disease [ 26 ] and PD [ 27 – 29 ]. Particularly, with regard to PwPD, most of the published studies investigated two-group gait pattern classifications, differentiating PwPD from healthy subjects [ 28 , 30 , 31 ], or performed multiclass classification according to the disease severity using the Unified Parkinson’s Disease Rating Scale [ 32 , 33 ]. However, none of these previous studies specifically searched for those gait parameter features able to categorize the gait pattern according to disease progression using the Hoehn and Yahr (H-Y) staging system [ 34 ].…”

Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint–Node Plots

“…The proposed methods were compared with previously developed methods with respect to the results listed in Table 5 . SVMs, random forest models, and cohorts have been applied to detect motor [ 48 , 49 ], balance, or gait function [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. The highest accuracy in classifying motor function was 97%, achieved by an SVM.…”