Micro-motion enhanced multi-person activity recognition with millimeter-wave radar

As a non-contact sensing device, millimeter-wave radar exhibits unique strengths in human activity recognition (HAR). Existing methods rely on micro-Doppler signatures for activity classification, but they often encounter feature aliasing in multi-person activity recognition (MPAR) scenarios. Although point cloud-based approaches can distinguish individual targets, they primarily extract static morphological features, neglecting the micro-motion information of human joints, which is crucial for accurate activity recognition. To address these limitations, we proposes an innovative MPAR framework that integrates spatial point clouds and micro-motion features. First, an improved point cloud data association algorithm is applied to achieve multi-target point cloud feature separation, followed by a dynamic projection mechanism to construct time–Doppler feature maps. Then, a torso micro-motion enhancement algorithm is designed to enhance the details of human body movements. Finally, a CNN-LSTM hybrid network architecture with a temporal-attention is constructed for action classification. Experimental results show that the proposed micro-motion enhancement algorithm improves recognition accuracy by 27.1% and 2.3%, compared to two traditional time–frequency analysis methods. Furthermore, MPAR task in occlusion scenarios achieves recognition accuracy of 93.5%. In summary, proposed framework not only retains the inherent advantages of millimeter-wave radar but also significantly enhances multi-person activity recognition in complex scenarios.