MoCoNet: Motion-Aware Convolution for Wi-Fi-Based Multi-User Activity Recognition

Abstract

Multi-user WiFi-based human activity recognition (HAR) with channel state information (CSI) is challenging because the received CSI contains overlapping motion-induced channel variations from multiple users, which complicates robust per-user activity inference. In this letter, we propose a motion-aware convolution framework that introduces signal-guided local aggregation for Multi-user WiFi CSI HAR. Compact motion cues are extracted from CSI phase and used to modulate convolution along temporal, subcarrier, and joint directions. This enables the model to emphasize coherent local CSI patterns within mixed multi-user observations, helping learn more discriminative and interference-aware CSI representations for multi-user HAR. Experiments on the WiMANS benchmark demonstrate that MoCoNet is an effective and complexity-balanced design, achieving 89.56% average accuracy under the standard environment-band evaluations, where it consistently outperforms representative baselines across the reported environment-band settings. In addition, under the 5 GHz leave-one-environment-out evaluation, MoCoNet achieves the highest average accuracy of 73.17% among the compared baselines.