A Real-time Hand Gesture Recognition System using 24 GHz Radar Array

This paper presents a description of a real-time hand gesture recognition system. This system consists of three commercial modules perpendicular mounted in an three-dimensional array to provide six-channel baseband I/Q signals. The I/Q signals are pre-processed by the doppler signal amplitude threshold detection and spectral analysis. A convolutional neural network consisting in two convolutional layers and two fully connected layers is constructed as the recognition classifier with less dependence of feature extraction. The network is trained with 1000 groups of datasets and verified by testing recognized results as the customized shortcut keys. Results show that this system could achieve a high recognition accuracy rate higher than 95% in the real-time test.