Human Gesture Recognition Based on Millimeter-Wave Radar Using Improved C3D Convolutional Neural Network

In this paper, we propose a time sequential IC3D convolutional neural network approach for hand gesture recognition based on frequency modulated continuous wave (FMCW) radar. Firstly, the FMCW radar is used to collect the echoes of human hand gestures. A two-dimensional fast Fourier transform calculates the range and velocity information of hand gestures in each frame signal to construct the Range-Doppler heat map dataset of hand gestures. Then, we design an IC3D network for feature extraction and classification of the dynamic gesture heat map. Finally, the experiment results show that the gesture recognition system designed in this paper effectively solves the problems of the difficulty of human gesture feature extraction and low utilization of time series information, and the average recognition accuracy rate can reach more than 99.8%.