The Power of Periodicity: Exploiting Periodic UWB CIRs for Robust Activity Recognition with Attention-aware Multi-level Wavelet

In recent years, wireless sensing techniques, such as Wi-Fi and Ultra-Wideband (UWB) signals, have gained attention for activity recognition due to their ability to address privacy concerns associated with traditional computer vision methods. While UWB Channel Impulse Response (CIR) is believed to be prominent approach as well as Wi-Fi Channel State Information (CSI), research on its application in activity recognition remains limited. Previous studies have not fully explored the brevity of single measurements or the potential for feature extraction from CIRs. This paper presents a novel approach to robust device-free activity recognition by exploiting periodic UWB CIR samples. Utilizing multi-level wavelet packet decomposition (WPD) and a customized attention mechanism, the proposed method effectively combines multi-resolution features, improving recognition accuracy and reducing the need for extensive fine-tuning. Experiments conducted in various scenarios validate the performance of the proposed approach, with ablation studies demonstrating the superiority of multi-resolution analysis over short-time Fourier transform (STFT) and highlighting the cost efficiency of the method.