Abstract: Due to the individual difference of surface electromyography (sEMG),an individual person always needs long-time pre-training for obtaining his own accurate classification model when using sEMG as control source of external equipment.For solving this problem,on the basis of the original KKT-SVM incremental learning method,a new SVM incremental learning algorithm (D-ISVM) based on DBSCAN density clustering was proposed and it was applied in the on-line sEMG hand gesture recognition.Firstly,considering that the new samples and initial non-SV samples can affect new SV set,the closeness of sample distribution is analyzed and clustered according to DBSCAN,and the new samples and initial non-SV samples which are close to initial SV set are selected.Then,these samples are furtherselec-ted based on core point and distance between samples and hyperplane.Finally,all selected samples and initial SV set are trained together to obtain new SV set.The experimental results show that,compared with general algorithms,the proposed D-ISVM incremental learning algorithm can achieve higher classification accuracy and further improve the learning speed of classification model.This method can effectively solve the individual difference problem during the on-line sEMG hand gesture recognition.

Key words: Hand gesture recognition, Incremental learning, sEMG, SVM