Radar Point Clouds Processing for Human Activity Classification using Convolutional Multilinear Subspace Learning

Radar-based human activity classification is crucial for applications such as healthcare monitoring, fall detection, and assisted living due to its superior sensing capabilities and privacy protection. Traditional classification methods generally retrieve features from the time-range domain or the time-frequency (TF) domain. Such 2-D representation neglects the underlying dependence between the three radar signal variables of time, range, and Doppler frequency, and cannot fully depict the dynamic human motion features. In this article, we propose a time-range-Doppler radar point clouds (RPCs)-based learning model for human activity classification using a frequency-modulated continuous waveform (FMCW) radar. The human echoes are first transformed into a series of 3-D point cloud cubes integrating the motion signatures in three domains, namely time-range, time-Doppler, and range-Doppler domains. The generated RPC cubes are then fed into a newly developed two-layer convolutional multilinear principal component analysis network (CMPCANet) for feature extraction and motion classification. The CMPCANet comprises a simple network architecture with small training parameters, and can be directly implemented on the 3-D tensor dataset to extract highly discriminative features. Experimental results demonstrate that proposed framework can achieve superior classification accuracy and noise robustness compared to other methods using multidomain information, even with small training samples.