Research on EMG Signal of Human Lower Limbs Based on Empirical Mode Decomposition

Abstract

To recognize Electromyography (EMG) signal of lower limbs more effectively, this paper analyzed EMG signal of gastrocnemius muscle during dorsal flexion, plantar flexion, knee flexion, knee flexion and hip flexion; Based on advantages of Empirical Mode Decomposition (EMD) in nonlinear and non-stationary signal analysis, Intrinsic Mode Function (IMF) was utilized as eigenvalue of EMG signal of gastrocnemius muscle; Support Vector Machine (SVM) was applied to verify influence on different orders of IMF in the classification effect. Results shown that IMF of gastrocnemius muscle under different movements is different. The orders of ankle dorsal flexion and plantar flexion are 5, knee flexion and extension are 6, and hip flexion is 7; when eigenvalues of higher order are discarded, the recognition rate for 5 movements is low (81.46%); The value is higher when eigenvalue matrix is supplemented by 0 (85.3%). the recognition rate is the highest when combining this two methods (90.42%).