RFRN: Cross-domain RFID Activity Recognition Using a Few Samples

Performing cross-domain activity recognition using a few samples is a challenge in the field of radio frequency identification (RFID) sensing. Due to multipath effect, RF signals collected from different domains exhibit unbalanced label distribution and heterogeneous signal distribution, which leads to domain shift problem. Moreover, constrained by cost, the labeled data available for training in each domain is limited. In this paper, we propose RFRN, an RFID activity recognition system based on Relation Network, and improve the recognition accuracy through several designs. First, the structure of Relation Network is improved to extract activity-related features, filter domain-related interference and then match different activities without additional fine-tuning. Second, a new task generation strategy is proposed to make full use of source domains and enable the model to experience more tasks. Experiments demonstrate that RFRN can effectively handle the domain shift problem and adapt well to new domains with a few samples. With one and five samples of each activity, RFRN outperforms the baselines by at least 7.3% and 4.0% on a real-world dataset with balanced label distribution and around 22.2% and 7.7% on a dataset with unbalanced label distribution, respectively.