Prediction of Lower Limb Joint Angle Based on VMD-ELMAN Electromyographic Signals
Surface electromyography(sEMG)signals are generated in advance of human movements and are commonly used to predict human behavior and motor intentions.However,due to its inherent non-stationary and time-varying characteristics,it is difficult to accurately predict changes in the angle of human lower limb.This paper presents a VMD-ELMAN angle fitting algo-rithm for muscle selection of human lower limb muscles for three movements:normal walking,ascending stairs and descending stairs.This algorithm improves the accuracy of surface electromyography signal angle prediction,enhances the real-time perfor-mance of angle prediction,and provides an effective solution for improving human-machine integration with exoskeleton devices.The Experimental results show that compared to common angle fitting algorithms,the proposed algorithm is less time-consu-ming.Among the three common movements,the highest accuracy of the hip joint angle prediction value RMSE is 0.5789,and the knee joint angle prediction value RMSE is within 0.2.Its prediction accuracy is superior to common models,and the model has strong robustness.
万方数据
维普
