A novel TCNet for irrelevant gesture rejection based on electromyography signals

Irrelevant gestures can cause myoelectric control systems to generate erroneous commands. To address the issue of irrelevant gesture interference, a temporal neural network based on center loss called TCNet is proposed. First, in order to mine the information between and within different periods of EMG signals, Fourier transform is used to decompose different frequency components of EMG signals, and the decomposed period is used to fold one-dimensional EMG signals in two dimensions. Second, two-dimensional convolution is used to learn the information between and within different periods of two-dimensional EMG signals, so that the model can better understand the temporal characteristics of EMG signals under different periodicities. Third, center loss is introduced to train the network, pushing features toward the center positions of the same class. This promotes small intra-class differences, facilitating better discrimination between irrelevant gestures and target gestures. Fourth, integrating Softmax output probability threshold rejection mechanism enables effective rejection of irrelevant gestures. This study focuses on 17 classes of gestures from 11 subjects, achieving a recognition rate of 91.5% for 10 target gesture classes, a rejection rate of 90.5% for 7 irrelevant gesture classes, and an average accuracy of 91.1%. This result is quite competitive compared to existing studies.