Volume 10 Issue 4
Aug.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(4): 656-664
YUAN Zhian, ZHOU Xiaoyu, LIU Xinpu, et al. Human fall detection method using millimeter-wave radar based on RDSNet[J]. Journal of Radars, 2021, 10(4): 656–664. doi: 10.12000/JR21015
Citation: YUAN Zhian, ZHOU Xiaoyu, LIU Xinpu, et al. Human fall detection method using millimeter-wave radar based on RDSNet[J]. Journal of Radars, 2021, 10(4): 656–664. doi: 10.12000/JR21015
shu

Human Fall Detection Method Using Millimeter-wave Radar Based on RDSNet

DOI: 10.12000/JR21015
  • 1.

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

  • 2.

    College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (61871386), The Natural Science Fund for Distinguished Young Scholars of Hunan Province (2019JJ20022)
More Information
  • Corresponding author: LU Dawei, davidloo.nudt@gmail.com
  • Received Date: 2021-02-26
  • Rev Recd Date: 2021-07-13
    • Available Online: 2021-07-26
  • Publish Date: 2021-08-28
    Abstract
  • With the advent of the aging population, fall detection has gradually become a research hotspot. Aiming at the detection of human fall using millimeter-wave radar, a Range-Doppler heat map Sequence detection Network (RDSNet) model that combines the convolutional neural network and long short-term memory network is proposed in this study. First, feature extraction is performed using the convolutional neural network. After obtaining the feature vector, the feature vector corresponding to the dynamic sequence is inputted to the long short-term memory network. Subsequently, the time correlation information of the heat map sequence is learned. Finally, the detection results are obtained using the classifier. Moreover, diverse human movement information of different objects is collected using millimeter-wave radar, and a range-Doppler heat map dataset is built in this work. Comparative experiments show that the proposed RDSNet model can reach an accuracy of 96.67% and the calculation delay is not higher than 50 ms. The proposed RDSNet model has good generalization capabilities and provides new technical ideas for human fall detection and human posture recognition.

     

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