Abstract: Currently, gesture recognition based on electromyography (EMG) signals primarily adopts image representation methods, making it difficult to capture the interactions between multiple channel signals through global feature extraction of images. This paper proposes a gesture recognition framework based on channel graph convolution (CGC), which aims to explore the relationships between multiple channel signals. Firstly, the paper proposes a circular graph representation of EMG signals, organically connecting signals from adjacent channels. Then, it performs graph convolution on the circular graph, and important features are adaptively selected through graph pruning. Finally, it conducts feature fusion for gesture recognition. The framework is evaluated on two publicly available benchmark datasets, Ninapro DB1 and Ninapro DB2, achieving recognition accuracies of 84.26% and 86.42%, respectively, demonstrating state-of-the-art performance.
Keywords: gesture recognition; graph neural networks; graph convolution; electromyography(EMG) signals; deep learning(DL)
合肥工业大学学报自科版