Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks

Abstract

Surface electromyography (EMG) signals have been widely used in locomotion studies and human-machine interface applications. In this paper, a regression model which relates the multichannel surface EMG signals to human lower limb flexion/extension (FE) joint angles is constructed. In the experimental paradigm, three dimensional trajectories of 16 external markers on the human lower limbs were recorded by optical motion capture system and surface EMG signals from 10 muscles directly concerned with the lower limb motion were recorded synchronously. With the raw data, the joint angles of hip, knee and ankle were calculated accurately and the time series of intensity for surface EMG signals were extracted. Then, a deep belief networks (DBN) that consists of restricted Boltzmann machines (RBM) was built, by which the multi-channel processed surface EMG signals were encoded in low dimensional space and the optimal features were extracted. Finally, a back propagation (BP) neural network was used to map the optimal surface EMG features to the FE joint angles. The results show that, the features extracted from multichannel surface EMG signals using DBN method proposed in this paper outperform principal components analysis (PCA), and the root mean square error (RMSE) between the estimated joint angles and calculated ones during human walking is reduced by about 50%. The proposed model is expected to develop human-machine interaction interface to achieve continuous bioelectric control and to improve motion stability between human and machine, especially for lower limb wearable intelligent equipment.