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Accurate hand gesture prediction is crucial for effective upper-limbprosthetic limbs control. As the high flexibility and multiple degrees offreedom exhibited by human hands, there has been a growing interest inintegrating deep networks with high-density surface electromyography (HD-sEMG)grids to enhance gesture recognition capabilities. However, many existingmethods fall short in fully exploit the specific spatial topology and temporaldependencies present in HD-sEMG data. Additionally, these studies are oftenlimited number of gestures and lack generality. Hence, this study introduces anovel gesture recognition method, named STGCN-GR, which leveragesspatio-temporal graph convolution networks for HD-sEMG-based human-machineinterfaces. Firstly, we construct muscle networks based on functionalconnectivity between channels, creating a graph representation of HD-sEMGrecordings. Subsequently, a temporal convolution module is applied to capturethe temporal dependences in the HD-sEMG series and a spatial graph convolutionmodule is employed to effectively learn the intrinsic spatial topologyinformation among distinct HD-sEMG channels. We evaluate our proposed model ona public HD-sEMG dataset comprising a substantial number of gestures (i.e.,65). Our results demonstrate the remarkable capability of the STGCN-GR method,achieving an impressive accuracy of 91.07% in predicting gestures, whichsurpasses state-of-the-art deep learning methods applied to the same dataset.

A Spatio-Temporal Graph Convolutional Network for Gesture Recognition from High-Density Electromyography