research-article

GRfid: A Device-Free RFID-Based Gesture Recognition System

Authors:

Lionel M. NiAuthors Info & Claims

IEEE Transactions on Mobile Computing, Volume 16, Issue 2

Pages 381 - 393

https://doi.org/10.1109/TMC.2016.2549518

Published: 01 February 2017 Publication History

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.

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cover image IEEE Transactions on Mobile Computing

IEEE Transactions on Mobile Computing Volume 16, Issue 2

February 2017

295 pages

ISSN:1536-1233

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Copyright © 2017.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 February 2017

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https://dl.acm.org/doi/10.1145/3636534.3649389
Sun XXiong JFeng CLi XZhang JLi BFang DChen XGanesan DShi W(2024)Gastag: A Gas Sensing Paradigm using Graphene-based TagsProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649365(342-356)Online publication date: 29-May-2024
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