CGAC: A Convolutional Bidirectional GRU Network with Temporal Attention for WiFi CSI-Based Human Activity Recognition

Abstract

WiFi channel state information (CSI) can characterize wireless-channel variations induced by human activities without directly capturing identifiable visual content, providing a contactless technical approach to indoor human activity recognition (HAR). To address the difficulty of a single convolutional or recurrent network in simultaneously modeling local fluctuations, long-range temporal dependencies, and key action segments, this paper proposes CGAC, a model that integrates convolutional bidirectional gated recurrent units with temporal attention. The model first uses one-dimensional convolution and max pooling to extract and compress local temporal CSI features, then employs a BiGRU to model bidirectional contextual dependencies, and finally applies single-vector temporal attention to adaptively weight key time steps. Multi-dataset evaluations are conducted on three public datasets: UT-HAR, NTU-Fi HAR, and NTU-Fi Human-ID. CGAC achieves an accuracy of 99.70% on UT-HAR and accuracies of 97.50% and 97.81% on NTU-Fi HAR and NTU-Fi Human-ID, respectively. The results show that CGAC delivers the best performance on UT-HAR and remains competitive across different acquisition tools and CSI classification tasks.