TY - JOUR ID - TI - Classification of EMG signals based on CNN features with ‎continuous wavelet transformation and LS-SVM AU - Shafaa Mahmood. Shnawa AU - Firas Sabar Miften PY - 2022 VL - 17 IS - 2 SP - 9 EP - 29 JO - Univesity of Thi-Qar Journal مجلة جامعة ذي قار العلمية SN - 27066894 27066908 AB - The various hand EMG signal grasps are classified in this study. ‎Because EMG signals offer critical information about muscle activity, ‎they are commonly used as input to electro muscular control systems. ‎Each muscle performs a specific function in each movement. ‎Electromyography is a medical, healthcare, and human-machine ‎interaction diagnostic technique for acquiring an EMG signal (MMI). e ‎most important component of the locomotion system is the muscular ‎system. Accordingly, sensors were developed to detect the movement ‎system and diagnose the electromyogram. Nowadays, While ‎maintaining a modest size, it has improved and become more accurate. ‎In this paper, The EMG signals are converted into images using CWT, ‎then the EMG images features are extracted based on convolutional ‎neural network (CNN) , and finally, the EMG features are categorized ‎by an LS-SVM classifier in Matlab. The main objective of this study is ‎to classify grasps into six basic hand movements: (1) cylindrical, (2) ‎palm, (3) lat (4) sphere(5) Tip, and (6) Hook. Finally, ‎electrophysiological patterns of each movement were extracted by ‎extracting features from the images using CNN where EMG images are ‎divided into (70 percent ) training and (30 percent ) validation, and ‎then ‎these features are fed into classification using the least square support ‎vector machine. It produced an accuracy of 95.33%.‎

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