SEMG Signal Classification Using Ensemble Learning Classification Approach and DWT

Abstract

Nowadays surface electromyography (sEMG) signals play a very authoritative role in facilitating a neuromuscular disordered person and disabled person to live a smooth life. This offline study mainly focuses on the denoising, feature extraction and classification of the sEMG signals with discrete wavelet packet transform (DWT) with ensemble support vector machine (SVM) classification approach. In this work DWT (db 2, 4th level) is selected for denoising and TFD feature extraction to form feature vectors and soft thresholding method (minimax) was utilized and the threshold value was taken as 2.991. The classification accuracy is 98% is achieved at the better precision and speed of response for elbow movement. feature vectors and soft thresholding method (minimax) was utilized and the threshold value was taken as 2.991. The classification accuracy is 98% is achieved at the better precision and speed of response for elbow movement.