Comparison of five time series EMG features extractions using Myo Armband

Abstract

Feature extraction is meant to get representation and information that embedded in the signals, this is necessary to minimize complexity of implementation and reduce the cost of information processing. Recently, there are many methods for features extraction. This research is comparing five feature extractions from eight channels electromyography (EMG) signals that obtained from Myo Armband located on forearm muscles in order to get significant differences when hand do some movements. The time series features extraction that evaluated are Mean Absolute Value (MAV), Variance (VAR), Willison Amplitude (WAMP), Waveform Length (WL), and Zero Crossing (ZC). The variety of hand movement are fist, rest, half-fist, gun-point, and mid-finger fold. Moreover, the result shows that the rank of evaluated features extraction always showhs same results in four experiment, MAV is always giving the best performance WL. From this finding, MAV and WL are two recommendation for time series features extraction. This rank of time series features extraction gives worthiness when process information in future development research.