Any-Position Any-Direction Human Activity Recognition With Motion-Driven Data Generation Method via Monostatic Radar

Recent radar data generation studies for human activity recognition (HAR) always focus on statistic-driven methods guided by statistical parameters, such as mean, variance, or latent network features. However, they lack physical interpretability and are unable to generate targeted data with a specified position or direction. To address this issue, a novel motion-driven radar data generation method is proposed for Any-position Any-Direction (APAD) HAR using monostatic radar. First, a 5D point cloud, including time, range, Doppler, pitch, and azimuth angle features, is achieved as a human activity model sample from a radial recording by Fast Fourier Transform (FFT), Constant False Alarm Rate Detector (CFAR), and Direction of Arrival (DOA) angle estimation processing. In particular, a speed-aware Density-based Spatial Clustering of Application with Noise (VA-DBSCAN) algorithm is designed for point reduction under the consideration of non-uniform point cloud distributions. Then, new human activity samples from any starting position with any moving direction can be generated by translating and rotating the above model sample. The generated Doppler and energy features are corrected according to the viewing position and direction. Finally, a radar-based PointNet Long Short-Term Memory (PointNet-LSTM) network is adopted for the APAD-HAR. In contrast experiments, only 48 recorded samples were utilized as a data source to generate a training group with 27888 samples. Our proposed method demonstrated its feasibility and superiority with an average recognition accuracy rate of 92.11% by more than 25% improvement compared to other state-of-the-art methods.