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SONG Yongkun, YAN Tianxing, ZHANG Ke, et al. A lightweight human activity recognition method for ultra-wideband radar based on spatiotemporal features of point clouds[J]. Journal of Radars, in press. doi: 10.12000/JR24110
Citation: SONG Yongkun, YAN Tianxing, ZHANG Ke, et al. A lightweight human activity recognition method for ultra-wideband radar based on spatiotemporal features of point clouds[J]. Journal of Radars, in press. doi: 10.12000/JR24110
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A Lightweight Human Activity Recognition Method for Ultra-wideband Radar Based on Spatiotemporal Features of Point Clouds

DOI: 10.12000/JR24110
  • 1.

    College of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China

  • 2.

    College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  The Youth Fund Project of the Hunan Provincial Natural Science Foundation (2024JJ6065)
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  • Corresponding author: SONG Yongkun, songyk1118@163.com
  • Received Date: 2024-06-05
  • Rev Recd Date: 2024-07-24
    • Available Online: 2024-07-30
    Abstract
  • Low-frequency Ultra-WideBand (UWB) radar offers significant advantages in the field of human activity recognition owing to its excellent penetration and resolution. To address the issues of high computational complexity and extensive network parameters in existing action recognition algorithms, this study proposes an efficient and lightweight human activity recognition method using UWB radar based on spatiotemporal point clouds. First, four-dimensional motion data of the human body are collected using UWB radar. A discrete sampling method is then employed to convert the radar images into point cloud representations. Because human activity recognition is a classification problem on time series, this paper combines the PointNet++ network with the Transformer network to propose a lightweight spatiotemporal network. By extracting and analyzing the spatiotemporal features of four-dimensional point clouds, end-to-end human activity recognition is achieved. During the model training process, a multithreshold fusion method is proposed for point cloud data to further enhance the model’s generalization and recognition capabilities. The proposed method is then validated using a public four-dimensional radar imaging dataset and compared with existing methods. The results show that the proposed method achieves a human activity recognition rate of 96.75% while consuming fewer parameters and computational resources, thereby verifying its effectiveness.

     

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