EAR: Exploiting Uncontrollable Ambient RF Signals In Heterogeneous Networks For Gesture Recognition

Authors:
Zicheng Chi UMBC
Yao Yao UMBC
Tiantian Xie UMBC
Xin Liu UMBC
Zhichuan Huang UMBC
Wei Wang UMBC
Ting Zhu UMBC

Introduction:

In this paper, the authors explore how to leverage the ambient wireless trafic that i) generated by uncontrollable IoT devices and ii) sensed by ambient noise floor measurements for human gesture recognition. Specifically, we introduce our system EAR, which can conduct ifne-grained human gesture recognition using coarse-grained measurements (i.e., noise floor) of ambient RF signals generated from uncontrollable signal sources.We conducted extensive evaluations in both residential and academic buildings.Experimental results show that although EAR uses coarse-grained noise floor measurements to sense the uncontrollable signal sources, the signal sources can be distinguished with an accuracy up to 99.76%.

Abstract:

The exponentially increasing number of Internet-of-Thing (IoT) devices introduces a spectrum crisis in the shared ISM band. However, it also introduces opportunities for conducting radio frequency (RF) sensing using pervasively available signals generated by heterogeneous IoT devices. In this paper, we explore how to leverage the ambient wireless trafic that i) generated by uncontrollable IoT devices and ii sensed by ambient noise floor measurements (a widely available metric in IoT devices) for human gesture recognition. Specifically, we introduce our system EAR, which can conduct ifne-grained human gesture recognition using coarse-grained measurements (i.e., noise floor) of ambient RF signals generated from uncontrollable signal sources. We conducted extensive evaluations in both residential and academic buildings. Experimental results show that although EAR uses coarse-grained noise floor measurements to sense the uncontrollable signal sources, the signal sources can be distinguished with an accuracy up to 99.76%. Moreover, EAR can recognize fine-grained human gestures with high accuracy even under extremely low trafic rate (i.e., 4%) from uncontrollable ambient signal sources.

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