TITLE:
Human Activity Recognition Based on Frequency-Modulated Continuous Wave and DenseNet
AUTHORS:
Wenshuo Jiang, Yuqian Ma, Wencheng Zhuang, Zhongqiang Wu, Yiming Hua, Meng Li, Zhengjie Wang
KEYWORDS:
Human Behavior Recognition, Millimeter-Wave Radar, Convolutional Neural Networks, Wireless Signal
JOURNAL NAME:
Journal of Computer and Communications,
Vol.11 No.7,
July
14,
2023
ABSTRACT: With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread attention and become a hot research area. FMCW signals reflected by target activity can be collected, and human activity can be recognized based on the measurements. This paper focused on human activity recognition based on FMCW and DenseNet. We collected point clouds from FMCW and analyzed them to recognize human activity because different activities could lead to unique point cloud features. We built and trained the neural network to implement human activities using a FMCW signal. Firstly, this paper presented recent reviews about human activity recognition using wireless signals. Then, it introduced the basic concepts of FMCW radar and described the fundamental principles of the system using FMCW radar. We also provided the system framework, experiment scenario, and DenseNet neural network structure. Finally, we presented the experimental results and analyzed the accuracy of different neural network models. The system achieved recognition accuracy of 100 percent for five activities using the DenseNet. We concluded the paper by discussing the current issues and future research directions.