RF-OnlineHAR: A Real-Time RFID Activity Recognition Framework With Memory-Augmented Transformer

Radio Frequency Identification (RFID)-based human activity recognition (HAR) holds strong potential for ubiquitous sensing due to its capabilities of identity-awareness, privacy-preservation, and long-term passive monitoring. However, most existing methods are constrained to offline classification, which significantly limits their deployment in real-time scenarios. In this paper, we propose RF-OnlineHAR, a real-time HAR framework that enables online, frame-by-frame recognition directly on streaming RFID signals. To better capture short-term temporal characteristics, we introduce a novel Tag Activity Representation Frame (TARF) that jointly encodes the dynamics of physical-layer signals and the irregularity of tag responses. Furthermore, we present the Memory-Augmented Streaming Transformer Encoder (MASTE), which integrates local attention with dynamic memory to capture long-range dependencies across sliding windows, enabling context-aware real-time recognition. Experimental results demonstrate that RF-OnlineHAR achieves 98.61% accuracy on the offline CWNU-RDA dataset, outperforming existing state-of-the-art methods, and attains 94.72% mAP on RF-StreamHAR, a dedicated real-time dataset constructed for continuous HAR evaluation. The proposed framework contributes toward bridging the gap between algorithmic development and practical deployment of RFID-based activity recognition systems, and shows promising potential for applications in areas such as smart healthcare and eldercare.