A Real-time Digit Gesture Recognition System Based on mmWave Radar

Gesture communication is one of the most general communication methods in the world, with the obvious advantage of exchanging information without worrying about the borderline of different languages. Therefore, establishing a cost-effective way of capturing and understanding human gestures has long been a popular research topic regarding human-machine interaction, particularly in emerging scenarios such as smart cities, etc. In this paper, we propose a system based on a commercially available mmWave radar to recognize digits represented by the travel path of the human hand using a specially designed convolutional neural network (CNN) algorithm. We illustrate the proposed system is capable of recording the path of the moving hand in real-time at the cost of 1 transmitter, 2 receivers, and 2.78 GHz bandwidth from the mmWave radar. Our experimental results show that an average prediction accuracy of 98.8% is achieved in a validation test based on a 7:3 ratio split from existing dataset and an average prediction accuracy of 95.3% in generalization test using fresh data.