CNN-LSTM Network Based Prediction of Human Joint Angles Using Multi-Band SEMG and Historical Angles

Abstract

Active rehabilitation training can promote the neural reorganization and facilitate the rehabilitation of paralyzed patients. To provide safe and efficient active training based on rehabilitation robots, human motion intention should be recognized firstly, which can be implemented by prediction of human joint angles using sEMG. In this study, a novel CNN-LSTM model using multi-band sEMG fused with historical angles is proposed to improve the angle prediction accuracy. Eight models using sEMG signals of different numbers of frequency bands (1, 3, 5, 7) and fused or not fused with historical angles are designed and tested based on 10 subjects. The results show that, sEMG signals of suitable number of frequency bands can efficiently raise the prediction accuracy, and adding historical angles to the inputs can effectively eliminate the fluctuation of angle prediction and significantly improve the prediction accuracy. In particular, the average prediction error for the model based on 5-band sEMG and historical angles on the test data set is 0.784 degrees, which is accurate enough for practical application for the robot assisted rehabilitation.