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Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part II

Research Article

STAPointGNN: Spatial-Temporal Attention Graph Neural Network for Gesture Recognition Using Millimeter-Wave Radar

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-54528-3_11,
        author={Jun Zhang and Chunyu Wang and Shunli Wang and Lihua Zhang},
        title={STAPointGNN: Spatial-Temporal Attention Graph Neural Network for Gesture Recognition Using Millimeter-Wave Radar},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part II},
        proceedings_a={COLLABORATECOM PART 2},
        year={2024},
        month={2},
        keywords={Human-computer interaction Millimeter-wave radar Gesture recognition Graph neural network Attention mechanism},
        doi={10.1007/978-3-031-54528-3_11}
    }
    
  • Jun Zhang
    Chunyu Wang
    Shunli Wang
    Lihua Zhang
    Year: 2024
    STAPointGNN: Spatial-Temporal Attention Graph Neural Network for Gesture Recognition Using Millimeter-Wave Radar
    COLLABORATECOM PART 2
    Springer
    DOI: 10.1007/978-3-031-54528-3_11
Jun Zhang1, Chunyu Wang1, Shunli Wang1, Lihua Zhang1,*
  • 1: Academy for Engineering and Technology
*Contact email: lihuazhang@fudan.edu.cn

Abstract

Gesture recognition plays a pivotal role in enabling natural and intuitive human-computer interaction (HCI), finding applications in diverse domains such as smart homes, robot control, and virtual reality. Thanks to advances in computer vision, the most popular method currently is to use the camera for gesture recognition. However, the camera struggles to function properly in poor lighting and inclement weather, and risks invading privacy. Due to the robust and non-invasive features of millimeter-wave radar, gesture recognition based on millimeter-wave radar has received extensive attention from researchers in recent years. In this paper, we propose a novel graph neural network named STAPointGNN for gesture recognition using millimeter-wave radar. In order to better extract features in the spatial and temporal dimensions of point clouds collected by millimeter-wave radar, we designed a spatial-temporal attention mechanism based on graph neural network. We also propose a novel point flow embedding method to capture the motion features of the point clouds in adjacent frames. To verify the superiority of our method, we conduct experiments on two public millimeter-wave radar gesture recognition datasets. The results show that our model outperforms existing mainstream algorithms.

Keywords
Human-computer interaction Millimeter-wave radar Gesture recognition Graph neural network Attention mechanism
Published
2024-02-23
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-54528-3_11
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