ESECW method to process sEMG and its application in gait recognition

Surface electromyography(sEMG) signals mainly contain noise from around environment. The noise leads the problems of poor recognition rate of lower limb. In this paper, a novel method was proposed to processing sEMG signals, named empirical mode filtering and self-enhancement algorithm with classical wavelet (ESECW), which denoises interface noise of originnl signals and enhanced the recognition rate of lower limb motions. The ESECW algorithm consist of two parts: the raw signal was first processed by the empirical mode decomposition (EMD). In the second part, the background noise of the original signal is firstly reduced with the band-pass filter, and then processed by four level wavelet decomposition to obtain four layer of high-frequency signal components which use to calculating average energy of signal. Finally, the above two processed signals are multiplied together to obtain the mixed signal. Thus, the active segment of raw signals is derived. then, the extracted feature set is input to the SVM for lowe limb motion pattern recognition. The actual experimental analysis indicate that ESECW method has good adaptability to various motion patterns and does not to set a series thresholds.