Abstract
Many deep learning models have been proposed in radar-based human activity recognition (HAR) area. For radar-based HAR, generally, the raw radar data is first converted to a 2-D spectrogram by using short-time Fourier transform(STFT). All the existing DL models adopt 2-D convolutional neural networks as they treat the 2-D spectrogram the same as an optical image. In this paper, for the first time, the radar spectrogram is treated as a time-sequential vector, and a DL model composed of 1-D convolutional neural networks (1D-CNNs) and recurrent neural networks (RNNs) is proposed. The experiment results show that the proposed model can not only achieve the highest accuracy but also have the fewest number of parameters than that of existing 2-D CNN methods.