CentralNet Method for Human motion Recognition Based on Multi-feature Fusion of Millimeter Wave Radar

Abstract

Objective: In this research, we propose a human motion recognition method based on multi-feature fusion of millimeter Wave(mmWave) radar, which is implemented by CentralNet. Aiming at solving the problems that the method based on camera is susceptible to light and weather, and the flexibility of wearable devices is poor. Methods: Firstly, Radar data of moving human body is collected by millimeter-wave radar, and then we obtain Micro-Doppler Spectrogram(MDS) and Candence-Velocity Diagram(CVD) through time-frequency analysis of received signal. Next, the MDS and CVD were respectively put into the subnetwork of CentralNet which is a neural network designed to achieve feature fusion. Finally, we can implement human motion recognition through CentralNet. Result: The test data set verified that the proposed method could achieve classification of five kinds of human motions with accuracy of 98.96%. Conclusion: MDS and CVD are suited for extracting time-frequency features of signal. And CentralNet can effectively fuse multiple features, which leads to well classification performance. Significance: Compared to camera and wearable devices, human motion recognition based on radar has shown advantages in terms of accuracy, privacy, and robustness.