Surface EMG signal analysis based on the empirical mode decomposition for human-robot interaction

Surface Electromyography (SEMG) is the electrical manifestation of the neuromuscular activation associated with a contracting muscle. SEMG directly reflects the human motion intention; thus, they can be used as input information for human-robot interaction. Taking into account that SEMG signals are complex physiological signals, being nonlinear, non-stationary and non-periodic, myoelectric classification methods must take into account such characteristics to be more effective. Recently, a novel technique for analysis of nonlinear and non-stationary signals was successfully applied to several kinds of investigations including seismological and biological signals. This technique, named Hilbert-Huang Transform (HHT) is formed by two complementary tools, which are called empirical mode decomposition (EMD) and Hilbert spectrum (HS). This work proposes a novel EMD-based myoelectric pattern recognition technique to be applied in human-robot interaction. The process of feature extraction is performed by two steps, firstly, the EMD decomposes the input SEMG signal into a set of functions designated as Intrinsic Mode Function (IMF); and secondly, it is calculated for each resulting IMF the RMS (Root Mean Square) and the coefficients of a four-order autoregressive model. The process of classification based on a linear classifier (Linear Discriminant Analysis). Using a database of EMG signals, the proposed method was applied to classify human upper-limb motion via EMG signals. The database includes 8 recorded SEMG channels from forearm in the execution of 7 movements. The error of classification was 3.3%. Obtained results suggest that the proposed myoelectric pattern recognition technique may be applied in Human-Robot Interaction (HRI) to control external systems such an upper limb motor neuroprosthesis.