Towards Domain-independent Complex and Fine-grained Gesture Recognition with RFID

Abstract

Gesture recognition plays a fundamental role in emerging Human-Computer Interaction (HCI) paradigms. Recent advances in wireless sensing show promise for device-free and pervasive gesture recognition. Among them, RFID has gained much attention given its low-cost, light-weight and pervasiveness, but pioneer studies on RFID sensing still suffer two major problems when it comes to gesture recognition. The first is they are only evaluated on simple whole-body activities, rather than complex and fine-grained hand gestures. The second is they can not effectively work without retraining in new domains, i.e. new users or environments. To tackle these problems, in this paper, we propose RFree-GR, a domain-independent RFID system for complex and fine-grained gesture recognition. First of all, we exploit signals from the multi-tag array to profile the sophisticated spatio-temporal changes of hand gestures. Then, we elaborate a Multimodal Convolutional Neural Network (MCNN) to aggregate information across signals and abstract complex spatio-temporal patterns. Furthermore, we introduce an adversarial model to our deep learning architecture to remove domain-specific information while retaining information relevant to gesture recognition. We extensively evaluate RFree-GR on 16 commonly used American Sign Language (ASL) words. The average accuracy for new users and environments (new setup and new position) are $89.03%$, $90.21%$ and $88.38%$, respectively, significantly outperforming existing RFID based solutions, which demonstrates the superior effectiveness and generalizability of RFree-GR.