Lower Limb Motion Classification Using Energy Density Features of Surface Electromyography Signals’ Activation Region and Dynamic Ensemble Model

Abstract

Accurate classification of lower-limb movements based on surface electromyography (sEMG) signals is vital for wearable auxiliary devices in military and medical rehabilitation. It is difficult to accurately obtain the effective features of the active region and establish a reliable classification model of complex lower-limb motion modes due to the strong dynamic time variability of the sEMG signals. Thus, an onset point detection algorithm, namely the maximum interclass variance (MIV) algorithm, was proposed to determine an accurate activation signal region. Then, a variational modal correlation dimension (VMCD) algorithm was proposed for feature construction to obtain the energy density feature (EDF) of the activation region, which reflects the activation signal’s amplitude energy distribution state. Finally, a random sampling multi-classifier dynamic ensemble (RSDE) algorithm was proposed to rapidly construct an ensemble model by adapting to the feature changes of the sEMG signals. A total of 25 subjects were recruited to participate in the experiment. Compared with the other methods, the proposed method could accurately classify five lower-limb motions by collecting only the data from three lower-limb muscles. The average accuracy and response time were better than those of many existing classification methods. This indicates that the proposed method can improve the man-machine cooperative response speed, which is beneficial to enhance the application effect of sEMG auxiliary equipment.