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 recognition 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 information. Then, the CNN-LSTM network is used to realize 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 environment and random population, and the average recognition accuracy remains above 88.7%.