RFID-based gesture recognition has gained considerable attention in recent years due to the cost-effectiveness of RFID tags and their advantages in preserving visual privacy, providing convenience to users. Existing RFID-based gesture recognition systems typically require users to collect a large amount of training data for each gesture class, and once a new class is introduced, the entire recognition model needs to be retrained. This greatly limits their scalability for new gestures. In this article, we propose FewSense, a practical RFID sensing system that achieves accurate gesture recognition with a small number of training samples. To provide sufficient training samples for FewSense, we introduce a virtual sample generation method to achieve data augmentation. Based on the augmented training data, FewSense enables few-shot gesture recognition. With the introduction of a fine-tuning mechanism, FewSense can easily adapt to changing gesture classes. Real-world experiments demonstrate that even with only seven training samples, FewSense achieves 90% recognition accuracy.
Zhang et al. (Thu,) studied this question.