CTGR: A Dual-Branch Convolutional-Transformer Network for RFID-Based Contactless Gesture Recognition

Abstract. RFID-based gesture recognition, while overcoming vision-based limitations in privacy and environmental robustness, faces three key challenges: inadequate temporal dynamics modeling, disjointed local-global feature integration, and suboptimal fusion of complementary Received Signal Strength Indicator RSSI and phase signals. To address these limitations, we present CTGR, a dual-branch CNN-Transformer architecture for RFID gesture recognition. Our CTGR framework first establishes dual parallel input pathways for RSSI and phase signals, then processes each branch through synergistic components: the STC layer employing depthwise separable convolutions to extract noise-robust local features from both modalities, and the MATE module applies multi-head self-attention to capture global temporal dependencies in each signal domain. Finally, CTGR combines the processed RSSI and phase features through a strategic fusion mechanism that effectively integrates their complementary properties, enabling comprehensive modeling of gesture dynamics. Extensive experiments across diverse scenarios validate the method's exceptional effectiveness, achieving a 97.38 average accuracy on a 7-class gesture dataset. Compared with mainstream recognition algorithms, CTGR demonstrates superior robustness in adapting to diverse users, varying gesture speeds, and challenging environmental conditions, ensuring consistent performance across real-world scenarios. This work enhances RFID-based interaction systems through spatio-temporal feature fusion, offering practical solutions for robust human-machine interfaces in dynamic environments.