Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition

Hands are two of the most crucial organs and they play major role for human activities. Therefore, amputee people experience many difficulties in daily life. To overcome these difficulties, prosthetic hand is an effective solution. In order to automate the control of prosthetic hands, surface electromyogram (sEMG) signals and machine learning techniques play vital role. In this work, a novel ternary pattern and discrete wavelet (TP-DWT) based iterative feature extraction method is proposed. By using the proposed TP-DWT based feature extraction network, a sEMG signal recognition method is presented. The proposed TP-DWT based sEMG signal recognition method consists of channel concatenation, feature extraction using TP-DWT network, feature selection by using 2 leveled feature selection method and classification using 'conventional classifiers. The proposed method is tested by using a sEMG dataset, which were collected from amputee participants with 3 force levels (Low, Moderate, High). Four cases were studied to comprehensively evaluate the proposed TP-DWT based hand movements classification method with the sEMG signals. Based on the evaluations, the proposed TP-DWT based sEMG classification method achieved 99.14 % accuracy rate for all force levels by using k-nearest neighbor (k-NN) classifier with 10-fold cross validation. Moreover 97.78 %, 93.33 % and 92.96 % success rates are achieved for Low, Moderate and High force levels respectively. (C) 2020 Elsevier Ltd. All rights reserved.