Pattern recognition of electromyography (EMG) signal for wrist movement using learning vector quantization (LVQ)

1st South Aceh International Conference on Engineering and Technology

工业技术（总论）

Putra, D.S.^1 ; Weru, Y.U.W.^1 ; Fitriady, F.^2

Informatics Engineering, Politeknik Aceh Selatan, Tapaktuan

23711, Indonesia^1

Industrial Electronics Engineering, Politeknik Aceh, Banda Aceh

23119, Indonesia^2

关键词: Classification accuracy;    Electromyography signals;    EMG signal;    Feature extraction methods;    Human muscles;    Learning Vector Quantization;    Muscle contractions;    Training and testing;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/506/1/012020/pdf
DOI  :  10.1088/1757-899X/506/1/012020

学科分类：工业工程学

来源: IOP

【 摘 要 】

EMG is an electric signal in the human muscle layer. This signal is caused by muscle contraction activity. The main purpose of this study was to explore the pattern of electromyography signal for wrist movement in open finger extensor. The EMG can be recorded using a device called electromyography (EMG). It can be acquired by attaching an electrode to the surface of the skin and the electrode was capturing the raw of EMG signal. Volunteers involved in this study were six people where each individual have 10 datasets the EMG signals. The electrodes are installed in the lower arm muscles. The EMG raw signal was processed by normalizing zero-mean. After pre-processing, the EMG signal has been done a feature extraction process to get the EMG data which was be an input vector in Learning Vector Quantization (LVQ). The feature extraction method was mean absolute value (MAV), root mean square (RMS), minimum value (Min), maximum value (Max), variance (Var), standard deviation (STD), and length of data (LoD). This study indicates that the classification accuracy for training and testing data of the EMG signal for wrist movement in open finger extensor (OFE) and grasping finger extensor (GFE) was 70.83% and 83.33% respectively. Therefore, the EMG signal can be used for identifying muscle disorder, artificial hand control and biometric identity.