Power independent EMG based gesture recognition for robotics

A novel method for detecting muscle contraction is presented. This method is further developed for identifying four different gestures to facilitate a hand gesture controlled robot system. It is achieved based on surface Electromyograph (EMG) measurements of groups of arm muscles. The cross-information is preserved through a simultaneous processing of EMG channels using a recent multivariate extension of Empirical Mode Decomposition (EMD). Next, phase synchrony measures are employed to make the system robust to different power levels due to electrode placements and impedances. The multiple pairwise muscle synchronies are used as features of a discrete gesture space comprising four gestures (flexion, ex- tension, pronation, supination). Simulations on real-time robot control illustrate the enhanced accuracy and robustness of the proposed methodology.