ReActor: Real-time and Accurate Contactless Gesture Recognition with RFID

Shigeng Zhang, Chengwei Yang, Xiaoyan Kui, Jianxin Wang, Xuan Liu, Song Guo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Contactless gesture recognition has emerged as a promising technique to enable diverse smart applications, e.g., novel human-machine interaction. Among others, gesture recognition based on radio frequency identification (RFID) is preferred due to its prevalent availability, low cost, and ease in deployment. However, current RFID-based gesture recognition approaches usually use profile template matching to distinguish different gestures, making them suffer from large recognition latency and fail to support real-time applications. In this paper, we propose a real-time and accurate contactless RFID-based gesture recognition approach called ReActor. ReActor uses machine learning rather than time-consuming profile template matching to distinguish different gestures, and thus achieves both very low recognition latency and high recognition accuracy. The major challenge of our approach is to determine a set of suitable attributes that can preserve the profile features of the signals related to different gestures. We combine two types of attributes in ReActor: the statistics of the signal profile that characterize coarse-grained features and the wavelet (transformation) coefficients of the signal profile that characterize fine-grained local features, both of which can be calculated fast. Experimental results demonstrate that ReActor can recognize a gesture with average latency less than 51ms, two orders of magnitude faster than state-of-the-art approaches based on profile template matching. Furthermore, ReActor also achieves higher recognition accuracy than previous works due to its optimized attribute set.

Original languageEnglish
Title of host publication2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728112077
DOIs
Publication statusPublished - Jun 2019
Event16th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2019 - Boston, United States
Duration: 10 Jun 201913 Jun 2019

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
Volume2019-June
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference16th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2019
Country/TerritoryUnited States
CityBoston
Period10/06/1913/06/19

Keywords

  • contactless
  • gesture recognition
  • machine learning
  • radio frequency identification
  • real time

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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