Unknown Motion Rejection in Myoelectric Pattern Recognition Using Convolutional Prototype Network

Traditional myoelectric pattern recognition (MPR) systems usually perform well under laboratory conditions. However, in practice, unknown motions that do not appear in the training phase will inevitably disturb MPR systems, leading to dramatic performance degradation in motion classification. Generative and discriminative models are two representative ways to solve this problem in the literature. They only learn the patterns of known motions and reject unknown motion samples endowed with different patterns. However, the generative models often perform poorly in categorizing known motions compared with the discriminative models. While in contrast, the discriminative models always perform relatively inferiorly in distinguishing unknown motions. In this paper, a novel myoelectric control method based on the convolutional prototype network (CPN) is proposed. CPN combines the advantage of the generative model and the discriminative model in an end-to-end manner. It constructs multiple prototypes of known motion classes and identifies samples of known/unknown classes using the template (i.e., prototype) matching. The proposed method is evaluated using the high-density surface electromyography (HD-sEMG) data recorded while performing seven known and six unknown hand motions. Compared to the latest deep learning-based study, our proposed method can yield a significantly higher accuracy of 97.87% in identifying unknown motions and reduce training and testing time by more than 80%, indicating that it is a promising solution to realize robust myoelectric control in practice.