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J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

A gesture recognition framework based on channel graph convolution

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一种基于通道图卷积的手势识别框架

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), May.2026, Vol.49 No.5: 585-590

DOI: 10.3969/j.issn.1003-5060.2026.05.002

Authors

ZHANG Youwei, WANG Qingshan

(School of Mathematics, Hefei University of Technology, Hefei 230601, China)

Abstract and Keywords

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)

作者信息

张有为，王青山

(合肥工业大学数学学院，安徽合肥230601)

摘要和关键词

摘要: 目前基于肌电信号的手势识别工作主要采用图像表示方法, 而对图像进行全局特征提取难以捕捉多个通道信号间的相互影响。文章设计一种基于通道图卷积(channel graph convolution, CGC)的手势识别框架, 用以挖掘多个通道信号间的关系。首先提出肌电信号的环形图表示, 将相邻通道的手势信号有机地联系在一起; 然后对环形图进行图卷积提取特征, 并通过图的剪枝自适应选取重要特征; 最后特征融合, 进行手势识别。该框架在 2 个公开基准数据集 Ninapro DB1 和 Ninapro DB2 上进行了评估, 分别取得 84.26% 和 86.42% 的识别准确率, 性能达到先进水平。

关键词: 手势识别；图神经网络；图卷积；肌电信号；深度学习

Funding

安徽省自然科学基金资助项目（2208085MF165）

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