EMG signal feature extraction based on wavelet transform

In this paper, a multi-channel electromyogram acquisition system using programmable system on chip (PSOC) microcontroller was used to obtain the surface of EMG signal. Two pairs of single-channel surface electrodes were used to measure and record the EMG signal on forearm muscles. Then, different levels of Daubechies Wavelet family were performed to analyze the EMG signal. Finally, features in terms of root mean square, logarithm of root mean square, centroid of frequency, and standard deviation were used to extract the EMG signal. The experimental results show that root mean square feature extraction method exhibits better performance for extracting the EMG signal compared to the other features. In the future, our method can be utilized to control a mechanical arm in real-time processing.