Surface EMG-based human-machine interface that can minimise the influence of muscle fatigue

摘要

It is clear that the surface electromyographic-based (sEMG) human-machine interface (HM1) shows a reduction in stability when the muscle fatigue occurs. This paper presents an improved incremental training algorithm that is based on online support vector machine (SVM). The continuous wavelet transform is used to study the changes of sEMG when muscle fatigue occurs, and then the improved online SVM is applied for sEMG classification. The parameters of the SVM model are adjusted for adaptation based on the changes of sEMG signals, and the training data is conditionally selected and forgotten. Experiment results show that the presented method can perform accurate modelling and the training speed is increased. Furthermore, this method effectively overcomes the influence of muscle fatigue during a long-term operation of the sEMG-based HMI.