Signal Processing and Communications Applications Conference

Abstract

In this study, Electromyogram (EMG) signals obtained in Rectus Femoris and Biceps Femoris muscles are used to trigger Patela-T reflexes in spastic patients. 26 features are derived by calculating five features in both time and frequency domains from measured EMG signals and six features of Pendulum movement to recognize the normal person and patients with Ashworth 1Ashworth 2 spasticity levels. Finally, spasticity is graded by using k-nearest neighbor (k-NN) algorithm and AdaBoost method. The performance of the proposed study is evaluated by calculating accuracy, sensitivity, precision and specificity criteria. Simulation results are demonstrated that the study has crucial importance to assess and grade the spasticity.