| Summary: | Aiming at the problems of the noise impact on the parametric image of hand gestures, the difficulty of gesture feature extraction, and the inefficient utilization of continuous gesture time sequential information, we propose a time sequential inflated 3 dimensions (TS-I3D) convolutional neural network approach for hand gesture recognition based on frequency modulated continuous wave (FMCW) radar sensor. Specifically, the FMCW radar is used to acquire the hand gesture data, and the range and speed of the gesture in each frame signal are calculated by 2 dimensions fast Fourier transform. Then, the range-Doppler map (RDM) is generated based on the relationship between motion parameters and frequency. The interference in RDM caused by people and the external environment is filtered out and the peak of hand gesture in RDM is further enhanced by wavelet transform. Finally, TS-I3D network is designed to extract the range and speed change information of the continuous gestures. The experimental results show that the average recognition accuracy rate of the hand gestures of the proposed method is 96.17%.
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