Recognition and Severity Rating of Parkinson’s Disease from Postural and Kinematic Features During Gait Analysis with Microsoft Kinect

When diagnosing Parkinson’s disease (PD), medical specialists normally assess several clinical manifestations of the patient and rate a severity level according to established criteria. They refer to the Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), the most widely adopted scale for rating PD. Since gait patterns differ between healthy elders and those with PD, we implement a simple, low-cost clinical tool that can extract kinematic and postural features through Microsoft Kinect v2 sensor to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients, rated according to MDS-UPDRS Part IV for motor complications, and fourteen healthy paired subjects. Several gait cycles were extracted for each patient to improve the reliability of the methods and sixteen kinematic and postural features were considered. After preliminary feature selection, several classifier families were trained (both Support Vector Machine, SVM, and Artificial Neural Networks, ANN) and evaluated for the best solution. Results showed that the ANN classifier performed the best by reaching 89,40% of accuracy with only nine features in diagnosis PD and 95,02% of accuracy with only six features in rating PD severity.