Issue No. 02 - Feb. (2017 vol. 16)
ISSN: 1536-1233
pp: 381-393
Yongpan Zou , College of Computer Science and Software Engineering, Shenzhen University, P.R. China
Jiang Xiao , College of Computer Science and Software Engineering, Shenzhen University, P.R. China
Jinsong Han , Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi, P.R. China
Kaishun Wu , College of Computer Science and Software Engineering, Shenzhen University, P.R. China
Yun Li , Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi, P.R. China
Lionel M. Ni , University of Macau (UM), Avenida da Universidade, Taipa, Macau, China
ABSTRACT
Gesture recognition has emerged recently as a promising application in our daily lives. Owing to low cost, prevalent availability, and structural simplicity, RFID shall become a popular technology for gesture recognition. However, the performance of existing RFID-based gesture recognition systems is constrained by unfavorable intrusiveness to users, requiring users to attach tags on their bodies. To overcome this, we propose GRfid, a novel device-free gesture recognition system based on phase information output by COTS RFID devices. Our work stems from the key insight that the RFID phase information is capable of capturing the spatial features of various gestures with low-cost commodity hardware. In GRfid, after data are collected by hardware, we process the data by a sequence of functional blocks, namely data preprocessing, gesture detection, profiles training, and gesture recognition, all of which are well-designed to achieve high performance in gesture recognition. We have implemented GRfid with a commercial RFID reader and multiple tags, and conducted extensive experiments in different scenarios to evaluate its performance. The results demonstrate that GRfid can achieve an average recognition accuracy of $96.5$ and $92.8$ percent in the identical-position and diverse-positions scenario, respectively. Moreover, experiment results show that GRfid is robust against environmental interference and tag orientations.
INDEX TERMS
Radiofrequency identification, Gesture recognition, Hardware, Mobile computing, Performance evaluation, Software, Training
CITATION

Y. Zou, J. Xiao, J. Han, K. Wu, Y. Li and L. M. Ni, "GRfid: A Device-Free RFID-Based Gesture Recognition System," in IEEE Transactions on Mobile Computing, vol. 16, no. 2, pp. 381-393, 2017.
doi:10.1109/TMC.2016.2549518