Classification of Human Activity Based on Radar Signal Using 1-D Convolutional Neural Network

Previously, the 2-D convolutional neural networks (2-D-CNNs) have been introduced to classify the human activity based on micro-Doppler radar. Whereas these methods can achieve high accuracy, their application is limited by their high computational complexity. In this letter, an end-to-end 1-D convolutional neural network (1-D-CNN) is first proposed for radar-based sensors for human activity classification. In the proposed 1-D-CNN, the inception densely block (ID-Block) tailored for the 1-D-CNN is proposed. The ID-Block incorporated the three techniques: inception module, dense network, and network-in-network techniques. With these techniques, the proposed network not only achieve a high classification accuracy but also keep the computational complexity at a low level. The experiments results show that the classification accuracy of the proposed method is 96.1% for human activity classification that is higher than that of existing state-of-art 2-D-CNN methods while the computational speed of forward propagation is increased by about (2.71x to 29.68x) of the existing 2-D-CNN methods.