Application of PSO-RBF neural network in gesture recognition of continuous surface EMG signals

In view of the fact that independent gesture recognition cannot fully meet the natural, convenient and effective needs of actual human-computer interaction, this paper analyzes the current research status of gesture recognition based on EMG signal, and considers the practical application value of EMG signal processing in prosthetic limb control, mobile device manipulation and sign language recognition. Therefore, in this paper, the particle swarm optimization (PSO) algorithm is used to optimize the center value and the width value of the radial basis function in the RBF neural network. And the author uses the EMG signal acquisition device and the electrode sleeve to collect the four-channel continuous EMG signals generated by eight consecutive gestures. Then, the author performs noise reduction and active segment detection based on the summation, and extracts the well-known 5 time domain features. Finally, the data obtained are normalized and divided into training set and test set to train and test the classifier. Simulation experiments show that the RBF neural network which optimizes the center value and width value of radial basis functions via particle swarm optimization algorithm achieves a high recognition rate in continuous gesture recognition.