ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Research on passive human activity recognition using WiFi ambient signals

  • Affective Computing andAnHui Province Key Laboratory of Advanced Intelligence Machine, Hefei University of Technology, Hefei 230009,China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.04.008
  • Received Date: 12 March 2014
  • Accepted Date: 10 October 2014
  • Rev Recd Date: 10 October 2014
  • Publish Date: 30 April 2015
    Abstract

  • Abstract

    Although traditional k-nearest neighbor(K-NN) and Bagging can recognize effectively less human activities using WiFi ambient signal, recognition by either alone of the seven states, namely, empty, walking, sitting, standing, sleeping, falling and running, is not ideal. To improve recognition rates, a new algorithm, fusion algorithm, was designed. It significantly outperforms K-NN and Bagging in terms of recognition ratios in both single-subject and multi-subject experiments.

    Abstract

    Although traditional k-nearest neighbor(K-NN) and Bagging can recognize effectively less human activities using WiFi ambient signal, recognition by either alone of the seven states, namely, empty, walking, sitting, standing, sleeping, falling and running, is not ideal. To improve recognition rates, a new algorithm, fusion algorithm, was designed. It significantly outperforms K-NN and Bagging in terms of recognition ratios in both single-subject and multi-subject experiments.
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    • References(13)
  • [1]
    钱志鸿, 王义君. 面向物联网的无线传感器网络综述[J]. 电子与信息学, 2013, 35(1): 215-227.
    Qian Z H, Wang Y J. Internet of things-oriented wireless sensor networks review[J]. Journal of Electronics & Information Technology, 2013, 35(1): 215-227.
    [2]
    Zhang M, Sawchuk A. Human Daily Activity Recognition with Sparse Representation Using Wearable Sensors[J]. Biomedical and Health Informatics, IEEE Journal of 2013,17(3): 553-560.
    [3]
    Chen L M, Nugent C D, Wang H. A knowledge-driven approach to activity recognition in smart homes[J]. IEEE Transactions on Knowledge and Data Engineering. 2012, 24(6): 961-974.
    [4]
    徐川龙,顾勤龙,姚明海. 一种基于三维加速度传感器的人体行为识别方法[J]. 计算机系统应用, 2013, 22(6): 132-135.
    Xu C L, Gu Q L, Yao M H. Activity Recognition Method Based on Three-Dimensional Accelerometer[J]. Computer Systems & Applications, 2013, 22(6): 132-135.
    [5]
    Orphomma S, Swangmuang N. Exploiting the wireless RF fading for human activity recognition[C]// 10th International Conference on Electrical Engineering/Electronics, Computer, Telecomunications and Information Technology. Krabi, Thailand: IEEE Press, 2013: 1-5.
    [6]
    Sigg S, Scholz M, Shi S Y, et al. RF-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals[J]. IEEE Transactions on Mobile Computing, 2014, 13(4): 907-920.
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    Heredia B, Ocaa M, Bergasa L M, et al. People location system based on WiFi signal measure[C]// International Symposium on Intelligent Signal Processing. Alcala de Henares, Spain: IEEE Press, 2007: 1-6.
    [8]
    裴文莲, 詹林.Android 平台上WiFi技术在商场员工定位系统中的应用[J]. 计算机与现代化, 2013, (2): 159-162.
    Pei W L, Zhan L. Application of WiFi technology in staff positioning system on android platform[J]. JISUANJI YU XIANDAIHUA, 2013, (2): 159-162.
    [9]
    Koweerawong C, Wipusitwarakun K, Kaemarungsi K. Indoor localization improvement via adaptive RSS fingerprinting database[C]// International Conference on Information Networking. Bangkok, Thailand: IEEE Press, 2013: 412-416.
    [10]
    Abdellatif M, Mtibaa A, Harras K A, et al. GreenLoc:An energy efficient architecture for WiFi-based indoor localization on mobile phones[C]// International Conference on Communications. Budapest, Hungary: IEEE Press, 2013: 4425-4430.
    [11]
    Biswas J, Veloso M. Wifi localization and navigation for autonomous indoor mobile robots[C]// International Conference on Robotics and Automation. Anchorage, USA: IEEE Press, 2010: 4379-4384.
    [12]
    Vilaseca D I, Giribet J I. Indoor navigation using WiFi signals[C]// 4th Symposium and Conference on Embedded Systems. Buenos Aires, Argentine: IEEE Press, 2013: 1-6.
    [13]
    Pu Q F, Gupta S, Gollakota S, et al. Whole-home gesture recognition using wireless signals[C]// Proceedings of the 19th Annual International Conference on Mobile Computing & Networking. Miami, USA: ACM Press, 2013: 27-38.)
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    [1]
    钱志鸿, 王义君. 面向物联网的无线传感器网络综述[J]. 电子与信息学, 2013, 35(1): 215-227.
    Qian Z H, Wang Y J. Internet of things-oriented wireless sensor networks review[J]. Journal of Electronics & Information Technology, 2013, 35(1): 215-227.
    [2]
    Zhang M, Sawchuk A. Human Daily Activity Recognition with Sparse Representation Using Wearable Sensors[J]. Biomedical and Health Informatics, IEEE Journal of 2013,17(3): 553-560.
    [3]
    Chen L M, Nugent C D, Wang H. A knowledge-driven approach to activity recognition in smart homes[J]. IEEE Transactions on Knowledge and Data Engineering. 2012, 24(6): 961-974.
    [4]
    徐川龙,顾勤龙,姚明海. 一种基于三维加速度传感器的人体行为识别方法[J]. 计算机系统应用, 2013, 22(6): 132-135.
    Xu C L, Gu Q L, Yao M H. Activity Recognition Method Based on Three-Dimensional Accelerometer[J]. Computer Systems & Applications, 2013, 22(6): 132-135.
    [5]
    Orphomma S, Swangmuang N. Exploiting the wireless RF fading for human activity recognition[C]// 10th International Conference on Electrical Engineering/Electronics, Computer, Telecomunications and Information Technology. Krabi, Thailand: IEEE Press, 2013: 1-5.
    [6]
    Sigg S, Scholz M, Shi S Y, et al. RF-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals[J]. IEEE Transactions on Mobile Computing, 2014, 13(4): 907-920.
    [7]
    Heredia B, Ocaa M, Bergasa L M, et al. People location system based on WiFi signal measure[C]// International Symposium on Intelligent Signal Processing. Alcala de Henares, Spain: IEEE Press, 2007: 1-6.
    [8]
    裴文莲, 詹林.Android 平台上WiFi技术在商场员工定位系统中的应用[J]. 计算机与现代化, 2013, (2): 159-162.
    Pei W L, Zhan L. Application of WiFi technology in staff positioning system on android platform[J]. JISUANJI YU XIANDAIHUA, 2013, (2): 159-162.
    [9]
    Koweerawong C, Wipusitwarakun K, Kaemarungsi K. Indoor localization improvement via adaptive RSS fingerprinting database[C]// International Conference on Information Networking. Bangkok, Thailand: IEEE Press, 2013: 412-416.
    [10]
    Abdellatif M, Mtibaa A, Harras K A, et al. GreenLoc:An energy efficient architecture for WiFi-based indoor localization on mobile phones[C]// International Conference on Communications. Budapest, Hungary: IEEE Press, 2013: 4425-4430.
    [11]
    Biswas J, Veloso M. Wifi localization and navigation for autonomous indoor mobile robots[C]// International Conference on Robotics and Automation. Anchorage, USA: IEEE Press, 2010: 4379-4384.
    [12]
    Vilaseca D I, Giribet J I. Indoor navigation using WiFi signals[C]// 4th Symposium and Conference on Embedded Systems. Buenos Aires, Argentine: IEEE Press, 2013: 1-6.
    [13]
    Pu Q F, Gupta S, Gollakota S, et al. Whole-home gesture recognition using wireless signals[C]// Proceedings of the 19th Annual International Conference on Mobile Computing & Networking. Miami, USA: ACM Press, 2013: 27-38.)
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