Surface electromyogram signals classification based on bispectrum

Abstract

This paper bispectrum is used to classify human arm movements and control a robotic arm based on upper limb's surface electromyogram signals (sEMG). We use bispectrum based on third-order cumulant to parameterize sEMG signals and classify elbow flexion and extension, forearm pronation and supination, and rest states by an artificial neural network (ANN). Finally, a robotic manipulator is controlled based on classification and parameters extracted from the signals. All this process is made in real-time using QNX ® operative system.