A Light Residual CNN for Radar Gesture Recognition
Dongheng Jia, Xuan Lü, Yangyang Hao, Zelong Xiao · 2024
Gesture recognition based on radar technique offers unique advantages over traditional touch or camera-based methods for contact-less control, as the micro-Doppler signatures vary with changes in hand movements. In this article, a light residual convolutional neural network (CNN) is proposed for radar gesture recognition. Three residual modules of different structures are designed. A self-made dataset of $\mathbf{2 0 0 0}$ samples is collected using a commercial 24 GHz frequency modulated continuous wave (FMCW) radar module. It consists of time-frequency maps of nine hand movements and one empty gesture. Performance evaluation and ablation experiments are conducted on the dataset, and the metrics of precision, recall, accuracy, F1score, confusion matrix and running time are compared with four references. Experimental results indicate that the proposed LRCNN can accurately recognize gestures in real-time and achieve state-of-the-art performances. The source code and dataset are both uploaded on: https://github.com/Jiadongheng/gesture-recognition-matlab.git