Research on multiple features of sEMG combination and dimension reduction

Feature Extraction for sEMG signals is the key technique of the sEMG-based recognition system. More and more different types of features are extracted at present. Aiming at the problem of blind selection and low information utilization rate of features, an improved extraction method based on the multiple features combination and dimension reduction is proposed by this paper to build new features with high information proportion to promote the accuracy of recognition system. The basic sEMG features of time domain and AR model coefficients are used in this paper to be projected on the direction of largest error via PCA method, then the proportion and cumulative of the components on each direction are calculated for selecting the main components that contain most information to rebuild the feature vectors while reducing the dimension of the vectors. In the process of extraction, aiming at the contradiction between the stable principles of the signal in short time window and fluctuation of feature values, the length of the time window is optimized. Experiment shows that the feature extraction method proposed in this paper can promote the accuracy of recognition, which proves the efficiency of the improved method.