Gesture recognition with a low power FMCW radar and a deep convolutional neural network

Abstract

Gesture recognition with radar enables remote control of consumer devices such as audio equipment, television sets and gaming consoles. In this paper, experimental results of hand gesture recognition with a low power FMCW radar and a deep convolutional neural network (CNN) are presented. The FMCW radar operates in the 24 GHz ISM frequency band and has an effective isotropic radiated power level of 0 dBm. Since low power consumption is a key aspect for application in consumer devices, the FMCW radar has only one receive channel which is different from other FMCW radars with multiple receive channels that have been described in literature. The recognition of gestures is performed with a deep convolutional neural network that is trained and tested with micro-Doppler spectrograms yielding excellent recognition performance in a simple test case consisting of 3 different gestures. A comparison of the training and test results for an amplitude spectrogram and a complex-valued spectrogram as the CNN input shows that in this test case there is no major benefit of using the phase information in the spectrogram.