A Hand Gesture Recognition Method for Mmwave Radar Based on Angle-Range Joint Temporal Feature

As a sensor, millimeter-wave (mmWave) radar can realize the function of touchless gesture control, and it has become a hot research spot in the field of Human-Computer Interaction (HCI). This paper proposes a robust mmWave gesture recog-nition method, which can recognize gestures end-to-end with high accuracy in a complex environment. It is worth mentioning that the Angle-Range joint temporal (ART) feature is extracted from radar echoes to describe gestures, which is a 3D matrix feature including azimuth, distance and speed in-formation. Then, the CNN-LSTM network is used to real-ize gesture classification. The experimental results show that this method has an accuracy of 98.5% for the recognition of four gesture types. The robust performance of the proposed method is validated by data samples collected in complex en-vironment and random population, and the average recognition accuracy remains above 88.7%.