Lightweight Deep Learning Model in Mobile-Edge Computing for Radar-Based Human Activity Recognition

Abstract

Radar-based human activity recognition (HAR) has great potential in many fields, such as surveillance, smart homes, and human–computer interaction. Complex deep neural networks have brought significant improvement in classification performance but also a surge of computational cost and the number of parameters, which makes it challenging to deploy in mobile devices. However, the existing studies in this area mainly focus on improving the classification accuracy. In this article, we propose an extremely efficient convolutional neural network (CNN) architecture named Mobile-RadarNet, which is specially designed for human activity classification based on micro-Doppler signatures. The new architecture exploits 1-D depthwise convolutions and pointwise convolutions to build lightweight CNN architecture. The experiments on a seven-class human activity data set demonstrate that the proposed Mobile-RadarNet can achieve high classification accuracy meanwhile to keep the computational complexity at an extremely low level, and thus has great potential to be deployed in the mobile devices.