Accurate hand gesture prediction is crucial for effective upper-limb prosthetic limbs control. As the high flexibility and multiple degrees of freedom exhibited by human hands, there has been a growing interest in integrating deep networks with high-density surface electromyography (HD-sEMG) grids to enhance gesture recognition capabilities. However, many existing methods fall short in fully exploit the specific spatial topology and temporal dependencies present in HD-sEMG data. Additionally, these studies are often limited number of gestures and lack generality. Hence, this study introduces a novel gesture recognition method, named STGCN-GR, which leverages spatio-temporal graph convolution networks for HD-sEMG-based human-machine interfaces. Firstly, we construct muscle networks based on functional connectivity between channels, creating a graph representation of HD-sEMG recordings. Subsequently, a temporal convolution module is applied to capture the temporal dependences in the HD-sEMG series and a spatial graph convolution module is employed to effectively learn the intrinsic spatial topology information among distinct HD-sEMG channels. We evaluate our proposed model on a 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, which surpasses state-of-the-art deep learning methods applied to the same dataset.

翻译：精准的手势预测对上肢假肢的有效控制至关重要。由于人手具有极高的灵活性和多自由度特性，将深度网络与高密度表面肌电（HD-sEMG）电极阵列相结合以提升手势识别能力的研究日益受到关注。然而，现有方法在充分挖掘HD-sEMG数据中特有的空间拓扑结构与时序依赖关系方面仍存在不足，且多数研究局限于有限数量的手势类型，缺乏泛化能力。为此，本研究提出一种名为STGCN-GR的新型手势识别方法，该方法利用时空图卷积网络构建基于HD-sEMG的人机交互接口。首先，我们依据通道间的功能连接性构建肌肉网络，形成HD-sEMG信号的图结构表征；随后，通过时序卷积模块捕捉HD-sEMG序列的时序依赖特征，并借助空间图卷积模块有效学习不同HD-sEMG通道间固有的空间拓扑信息。我们在包含65种手势的大规模公开HD-sEMG数据集上评估了所提模型。实验结果表明，STGCN-GR方法展现出卓越性能，手势预测准确率达到91.07%，超越了应用于同一数据集的最新深度学习方法。