CUTA-HAR: A Cross-User Temporal Attention Network for Wi-Fi CSI-Based Human Activity Recognition

Abstract

Wi-Fi-based human activity recognition (HAR) using channel state information (CSI) provides a nonintrusive and device-free sensing solution for smart cities, healthcare monitoring, and smart homes. However, recognition performance often degrades when models trained on limited users are applied to unseen users due to variations in body shape, posture, movement style, and surrounding conditions. To address this cross-user robustness challenge, this article proposes CUTA-HAR, a Cross-User Temporal Attention Network for Wi-Fi CSI-based HAR. CUTA-HAR combines multiuser supervised training with an attention-based bidirectional LSTM (BiLSTM) to capture informative temporal CSI patterns from multiple training users, without requiring data from the unseen test user during training. Experimental evaluations on a self-collected multiuser CSI dataset show that CUTA-HAR consistently outperforms representative sequence modeling baselines under a leave-one-user-out evaluation protocol, achieving average test accuracy improvements of 2.0%–6.7%. Action-level analysis further shows that structured activities can be recognized reliably, while complex activities such as fall and pickup remain challenging due to larger cross-user motion variations. These results indicate the effectiveness of attention-guided temporal modeling for improving cross-user robustness in Wi-Fi CSI-based HAR.