Radar-Based Human Activity Recognition With 1-D Dense Attention Network

With the development of the Internet of things, radar-based human activity recognition is becoming more and more important, because they play an indispensable role in fields such as safety and health monitoring. In this work, a novel network named 1-D dense attention neural network (1-D-DAN) is proposed for the radar-based human activity recognition. In the proposed network, a novel attention mechanism network structure specifically designed for radar spectrogram is proposed, equipping 1-D convolutional network with attention mechanism. With the x-axis of the spectrogram represents time and the y-axis represents frequency, the proposed attention mechanism includes two branches: 1) time attention branch and 2) frequency attention branch. Moreover, a dense attention operation that can make full use of features in the network is also introduced in the proposed attention mechanism. Experimental results show that compared with the state-of-the-art methods, our proposed 1-D-DAN achieves the highest accuracy in human activity recognition with the lowest computational complexity.