TransRF: Towards a Generalized and Cross-Domain RFID Sensing System Using Few-Shot Learning

RFID-based human activity recognition has attracted extensive attention due to its low cost, non-invasiveness, and privacy protection. However, existing methods may limit cross-domain sensing as the mapping between activities and signals is destroyed when the environment changes. Meanwhile, these methods lack generalization as their systems need to be fully retrained whenever new activities are added, which incurs data collection and retraining overheads. This paper proposes a few-shot learning-based RFID sensing system, TransRF, composed of a signal processing module, a feature extraction module, and a classification module. Specifically, to recognize novel classes in unknown domains with limited samples, the feature extraction module consists of multi-head self-attention and multi-scale hybrid dilated convolution and pre-train it with source domain data. Therefore, when the system is applied to the target domain, a few samples are required to fine-tune the classification module for domain adaptation. Experimental results on the genuine RFID dataset show that TransRF achieves an accuracy of 98.0% in unknown domains, a 31.4% improvement over the state-of-the-art. Additionally, we published our dataset containing three scenarios with 1.728 million pieces of data.