Robot Motion Governing Using Upper Limb EMG Signal Based on Empirical Mode Decomposition

This paper presents a simple and effective approach to govern robot arm motion in real time using upper limb EMG signals. Considering the non-stationary and nonlinear characteristics of the EMG signals, in the design for feature extraction, we introduce the empirical mode decomposition (EMD) to decompose the EMG signals into intrinsic mode functions (IMFs). Each IMF represents different physical characteristic, so that the muscular movement can be recognized. We then integrate it with a so-called initial point detection method previously proposed to establish the mapping between the upper limb EMG signals and corresponding robot arm movements in real time. In addition, for each individual user, we adopt a fuzzy approach to select proper system parameters for motion classification. The experimental results show the feasibility of the proposed approach with accurate motion recognition.