Myoelectric signal analysis using Hilbert-Huang Transform to identify muscle activation features

Abstract

Surface EMG signals have many important characteristics that could be useful to anticipate user's movements for orthotic and prosthetic devices. New methods for signal processing have appeared, but not all of them apply to non-linear and non-stationary processes such like myolectric signals. To perform a real-time analysis over these signals is important to simplify the processing and reduce time computing. Using the Empirical Mode Decomposition (EMD) method were obtained the Intrinsic Mode Functions (IMFs) for a multichannel signal to filter, discard and identify characteristics. sEMG signals were acquired using NI-DAQ system with four differential inputs for four forearm muscles. Seven hand movements were considered, that signals were recorded into a 4 × 20480 matrices in 1000 records. These records were divided in three segments: doss, transitory and contraction; through EMD method we obtained their IMFs to analyze with Hilbert transform. Results will be considered to simplify the sEMG signal for real-time analysis to control a prosthetic device.