WiMAR: A WiFi-Based Multi-User Human Activity Recognition System via Dynamic Component Separation

WiFi-based Human Activity Recognition (HAR) is a promising approach for non-invasive, device-free monitoring of user movements, particularly in smart home and healthcare applications. However, existing studies have mainly focused on single-user scenarios, which limits practicality in real-world environments and makes them vulnerable to performance degradation from wireless channel noise and interference. We propose WiFi Multi-user Human Activity Recognition (WiMAR), a system that separates the dynamic component of Channel State Information (CSI) induced by human activities from the static component caused by the environment. Our novel hybrid CNNRABGRUM framework combines Convolutional Neural Networks with Residual Structures (CNNR) and context-aware Bidirectional Gated Recurrent Units with an Attention mechanism and Multilayer Perceptrons (ABGRUM) to extract discriminative features from the dynamic CSI component. Experimental results show that WiMAR achieves a 2.9% increase in average recognition accuracy compared to state-of-the-art models, along with accelerated training convergence and enhanced stability, demonstrating robust performance in complex multi-user environments.¹