The Classification of Surface Electromyographic for Ankle Eversion and Inversion Based on Cerebellar Model Neural Networks

Abstract

The surface electromyography (sEMG) signal is the sum of the action potentials generated by the active motor units and detected over the skin, which has great performance on the recognition of human movements and the diagnosis of injuries because of its strong muscle specificity and differences in exercise patterns. This paper employs the Cerebellar model neural network (CMNN) as a classifier of the ankle motion based on sEMG. We testify our method on the data recorded from six healthy subjects who are on isokinetic ankle eversion and inversion. The results show that the classification accuracy is higher than 96.9% with less training times. For the future application, the CMNNs can be employed to predict the ankle motions in real-time to control exoskeleton robot.