Author: D S Putra, M A Ihsan, A D Kuraesin, Mustakim, Achmad Daengs and I B A I Iswara
Publish: 2nd International Conference on Advance & Scientific Innovation Journal of Physics: Conference Series 1424 (2019) 012013
Electromyography (EMG) signal is an myoelectric signal in the muscle layer. It occurs caused by contraction and relaxation muscle activity. This article provide numerical study of the classifying the electromyography signal for wrist movement combined with open and grasping finger flexor. The EMG signal has recorded using a device called electromyography. It has acquired by attaching an surface electrode in the skin then the electrode was capturing the raw signal. The volunteer involved were six where each volunteer has ten datasets the EMG signal. The surface electrode are sticked in the lower arm muscle. The EMG raw signal was processed using zero-mean normalization. The feature extraction method is root mean square (rms), mean absolute value (mav), variance (var), and standard deviation (std). This EMG signal has been classified by naïve bayes classifier. Training and testing data was using 5-cross validation. The result indicates that the classification accuracy for classifying the EMG signal for wrist movement combined open finger flexor (OFF) and grasping finger flexor (GFF) is 70% and 75% respectively. Therefore, the EMG signal can be applied for identificating of muscle disorder, prostheses hand and biometric system.
In this investigation has shown that the EMG signal in the lower arm muscle be able to used to identify people’s wrist movement. The EMG signal be able to specify the pattern recognition from one to another applied to artificial hand. The overall classification accuracy is 70% for OFF movement and 75% for GFF movement. The finding of this study suggest that the feature used in this study and naïve bayes classifier was appropriated method for studying the EMG signal classification. A further study could assess reducing the number of instances in each subjects to achieve better accuracy.