  Hand gesture recognition has long been a hot topic in human computer
interaction. Traditional camera-based hand gesture recognition systems cannot
work properly under dark circumstances. In this paper, a Doppler Radar based
hand gesture recognition system using convolutional neural networks is
proposed. A cost-effective Doppler radar sensor with dual receiving channels at
5.8GHz is used to acquire a big database of four standard gestures. The
received hand gesture signals are then processed with time-frequency analysis.
Convolutional neural networks are used to classify different gestures.
Experimental results verify the effectiveness of the system with an accuracy of
98%. Besides, related factors such as recognition distance and gesture scale
are investigated.
