• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Wang Xuan, Fang Hechuan, Chang Liqiong, Wang Ju, Chen Xiaojiang, Fang Dingyi, Peng Yao, Chen Feng. Research on Key Technologies of RFID based Device Free Gesture Recognition[J]. Journal of Computer Research and Development, 2017, 54(12): 2752-2760. DOI: 10.7544/issn1000-1239.2017.20160648
Citation: Wang Xuan, Fang Hechuan, Chang Liqiong, Wang Ju, Chen Xiaojiang, Fang Dingyi, Peng Yao, Chen Feng. Research on Key Technologies of RFID based Device Free Gesture Recognition[J]. Journal of Computer Research and Development, 2017, 54(12): 2752-2760. DOI: 10.7544/issn1000-1239.2017.20160648

Research on Key Technologies of RFID based Device Free Gesture Recognition

More Information
  • Published Date: November 30, 2017
  • As a vital component of human-computer interaction, gesture recognition has gained a lot of attentions in civilian applications recently. Many applications would benefit from such gesture recognition, e.g. smart phone, smart home system, Kinect, etc. In this work, we introduce an RFID (radio frequency identification) based solution to recognize gestures using COTS (commercial off-the-shelf) RFID tags and readers. The basic idea is treating signal features caused by perturbation of gestures as the fingerprint. Furthermore, we leverage multipath to increase the difficulty of matching, which provides a high recognition rate. Unlike past work, which required user to attach an RFID tag, our method does not require any device to be attached to users. Specifically, we solve the problem that RFID communication is discontinuous in time domain by data slicing. We then capture and extract characteristic matrix corresponding to each gesture by using a synthetic aperture radar (SAR). Finally, we adopt dynamic time warping (DTW) techniques to recognize gestures by matching with the priori gesture fingerprint database. We implement our method using a COTS RFID device and evaluate it with 6 commercial tags. Experimental results demonstrate that this device free method is feasible and it can achieve a correct recognition rate of about 85%.
  • Related Articles

    [1]Qiu Jiefan, Xu Yifan, Xu Ruiji, Zhou Dongli, Chi Kaikai. An Optimization Method of Human Vital Signs Detection During the Non-Steady States[J]. Journal of Computer Research and Development, 2024, 61(2): 481-493. DOI: 10.7544/issn1000-1239.202220774
    [2]Ye Jing, Zou Bowei, Hong Yu, Shen Longxiang, Zhu Qiaoming, Zhou Guodong. Negation and Speculation Scope Detection in Chinese[J]. Journal of Computer Research and Development, 2019, 56(7): 1506-1516. DOI: 10.7544/issn1000-1239.2019.20180725
    [3]Huang Jipeng, Shi Yinghuan, Gao Yang. Multi-Scale Faster-RCNN Algorithm for Small Object Detection[J]. Journal of Computer Research and Development, 2019, 56(2): 319-327. DOI: 10.7544/issn1000-1239.2019.20170749
    [4]Zhang Hu, Tan Hongye, Qian Yuhua, Li Ru, Chen Qian. Chinese Text Deception Detection Based on Ensemble Learning[J]. Journal of Computer Research and Development, 2015, 52(5): 1005-1013. DOI: 10.7544/issn1000-1239.2015.20131552
    [5]Lan Mengwei, Li Cuiping, Wang Shaoqing, Zhao Kankan, Lin Zhixia, Zou Benyou, Chen Hong. Survey of Sign Prediction Algorithms in Signed Social Networks[J]. Journal of Computer Research and Development, 2015, 52(2): 410-422. DOI: 10.7544/issn1000-1239.2015.20140210
    [6]Gu Mingqin, Cai Zixing. Traffic Sign Recognition Based on Parameter-free Detector and DT-CWT[J]. Journal of Computer Research and Development, 2013, 50(9): 1893-1901.
    [7]Zheng Liming, Zou Peng, Han Weihong, Li Aiping, Jia Yan. Traffic Anomaly Detection Using Multi-Dimensional Entropy Classification in Backbone Network[J]. Journal of Computer Research and Development, 2012, 49(9): 1972-1981.
    [8]Zheng Liming, Zou Peng, Jia Yan. Anomaly Detection Using Multi-Level and Multi-Dimensional Analyzing of Network Traffic[J]. Journal of Computer Research and Development, 2011, 48(8): 1506-1516.
    [9]Zhang Yuhe, Huang Xi, Cui Li. WSN Nodes for Real-Time Traffic Information Detection[J]. Journal of Computer Research and Development, 2008, 45(1): 110-118.
    [10]Zhang Liangguo, Gao Wen, Chen Xilin, Chen Yiqiang, Wang Chunli. A Medium Vocabulary Visual Recognition System for Chinese Sign Language[J]. Journal of Computer Research and Development, 2006, 43(3): 476-482.

Catalog

    Article views (1548) PDF downloads (534) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return