A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Abstract

Modern human activity recognition (HAR) systems are designed using large amounts of experimental data. So far, real-data-driven or experimental-based HAR systems using Wi-Fi or radar systems have shown considerable results. However, the acquisition of large, clean, and labeled training data sets remains a crucial impediment to the progress of experimental-based HAR systems. Therefore, in this article, a paradigm shift from the experimental to a fully simulation-based design of HAR systems is proposed in the context of radar sensing. An end-to-end simulation framework is proposed as a proof-of-concept that can simulate realistic millimeter-wave radar signatures for synthesized human motion. We designed a human motion synthesis tool that emulates different types of human activities and generates the spatial trajectories accordingly. These trajectories are processed by a geometric model with respect to user-defined antenna configurations. Considering the long- and short-time stationarity of wireless channels, we synthesize the raw in-phase and quadrature data and process the data to simulate the radar signatures for emulated human activities. Finally, a simulated and a real HAR data set were used to train and test a simulation-based HAR system, respectively, which gave an average (maximum) classification accuracy of 94% (98.4%). The main advantage of the proposed simulation framework is that the training effort for radar-based classifiers, e.g., gesture recognition systems, can be minimized drastically.