Feature Extraction of EMG Recognition Signal Under Various Different Positions of Hand Motion

Abstract

Electromyography (EMG) is an experiment-based method for evaluating and recording a series of the electrical signal that emanate from the body muscles and motor units are the smallest functional units of our movements. EMG is widely used in various fields to investigate muscular activities. Since EMG signals contain a wealth of the information about muscle functions, there are many other approaches in analyzing the EMG signals. It is important to know the features that can be extracting from the EMG signal. The ideal feature is important for achievement in the EMG analysis. Hence, the objective of this paper is to evaluate the feature extraction of time domain from the EMG signal. The experiment was setup according to the surface electromyography. This paper presents the detailed evaluation and classification of SEMG at different muscles of the body. The recorded data was analyzed in the time domain to get the features. Then, time-domain feature extraction methods average, RMS and SD are applied to signals. The results show that the extracted features of the EMG signals in time domain can be implement in signal classification. These findings could be integrated to design a signal classification based on features extraction. In this manner, the goal of this article is to evaluate the elements extraction of time area from the EMG flag. The test was setup.