Open AccessElectromyography (EMG) signal classification for wrist movement using naïve bayes classifier
Author(s) -
Darma Setiawan Putra,
Maulana Ihsan,
Arlis Dewi Kuraesin,
Mustakim Mustakim,
G. S. Achmad Daengs,
Ida Bagus Ary Indra Iswara
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1424/1/012013
Subject(s) - electromyography , wrist , pattern recognition (psychology) , naive bayes classifier , signal (programming language) , artificial intelligence , normalization (sociology) , computer science , standard deviation , root mean square , speech recognition , biomedical engineering , physical medicine and rehabilitation , mathematics , medicine , statistics , engineering , anatomy , support vector machine , sociology , anthropology , electrical engineering , programming language
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.