Hand gesture recognition method by radar based on convolutional neural network

With the widespread use of hand gesture recognition technique, capabilities of robust measurement and classification in non-contact and all-day conditions are much desired in its applications, such as human-computer interaction, life entertainment and medical service. According to this requirement, the paper introduces a hand gesture recognition method based on linear frequency modulated continuous wave (LFMCW) radar range-Doppler (RD) information and convolutional neural network (CNN). Firstly, for LFMCW radar echoes from hand gestures, dechirping, fast Fourier transform in fast-time domain and coherent integration are applied to produce the two-dimensional RD images of hand gesture. Next, they are used as the input data of CNN, and the feature space is constructed with the process of two-layer convolution and pooling. Finally, the effective hand gesture recognition is achieved by full connection and softmax classifier. On this basis, a 24 GHz industrial radar sensor is used to design the experimental system for hand gesture measurement, and a dataset of four typical hand gestures is also generated with the LFMCW waveform. The experimental results show that the proposed method based on RD information and CNN is applicable to general radar sensors at 24 GHz and could achieve effective recognition of typical hand gestures.