TSARDC-Net: A Temporal–Spatial Anisotropic Network for Human Activity Recognition Using 3-D Radar Data Cube

The application of the 3-D radar data cube (RDC) which integrates time, distance and Doppler frequency information for accurate human activity recognition (HAR), has attracted much recent research interest in the field of smart healthcare. However, existing methods often fail to fully exploit the temporal-spatial characteristics and the anisotropic nature of RDC, limiting their performance in HAR. To address these limitations, we propose a new temporal-spatial anisotropic radar data cube network (TSARDC-Net) for HAR. This network utilizes a convolutional neural networks-long short-term memory (CNN-LSTM) architecture to simultaneously extract spatial and temporal features from radar signals, aiming to obtain joint modeling of the temporal-spatial characteristics of human motion. We adopted a unique anisotropic multi-scale convolution (AMSC) module to address the anisotropic spatial distribution characteristics of RDC and enhance feature extraction capability. We also introduced Squeeze and Excitation normalization (SENM) to adjust the learned features, thereby improving the model’s ability to recognize action features. Furthermore, considering practical deployment requirements, we explored a lightweight strategy based on separable convolutions. We used a public dataset which includes 1,754 samples, recording 6 different human activities. In addition, we recruited a group of volunteers using an off-the-shelf WiFi radar device and obtained a dataset containing 2,148 samples of 5 different activities. TSARDC-Nets were trained separately on these two datasets. Experimental results show that, on the public dataset, the proposed method achieves a classification accuracy of 98.58%, outperforming existing methods. Additionally, the proposed method achieves an accuracy of 95.57% on our dataset, showing good generalization capability.