Wireless Sensing for Human Activity Recognition Using USRP

William Taylor, Syed Aziz Shah, Kia Dashtipour, Julien Le Kernec, Qammer H. Abbasi, Khaled Assaleh, Kamran Arshad, Muhammad Ali Imran

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review


    Artificial Intelligence (AI) in tandem wireless technologies is providing state-of-the-art techniques human motion detection for various applications including intrusion detection, healthcare and so on. Radio Frequency (RF) signal when propagating through the wireless medium encounters reflection and this information is stored when signals reach the receiver side as Channel State information (CSI). This paper develops an intelligent wireless sensing prototype for healthcare that can provide quasi-real time classification of CSI carrying various human activities obtained using USRP wireless devices. The dataset is collected from the CSI of USRP devices when a volunteer sits down or stands up as a test case. A model is created from this dataset for making predictions on unknown data. Random forest was able to provide the best results with an accuracy result to 96.70% and used for the model. A wearable device dataset was used as a benchmark to provide a comparison in performance of the USRP dataset.

    Original languageEnglish
    Title of host publicationBody Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings
    EditorsMasood Ur Rehman, Ahmed Zoha
    PublisherSpringer Science and Business Media Deutschland GmbH
    Number of pages11
    ISBN (Electronic)9783030955939
    ISBN (Print)9783030955922
    Publication statusE-pub ahead of print - 11 Feb 2022
    Event16th EAI International Conference on Body Area Networks, BODYNETS 2021 - Virtual, Online
    Duration: 25 Dec 202126 Dec 2021

    Publication series

    NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
    Volume420 LNICST
    ISSN (Print)1867-8211
    ISSN (Electronic)1867-822X


    Conference16th EAI International Conference on Body Area Networks, BODYNETS 2021
    CityVirtual, Online

    Bibliographical note

    Funding Information:
    Acknowledgement. William Taylor’s studentship is funded by CENSIS UK through Scottish funding council in collaboration with British Telecom. This work is supported in parts by EPSRC EP/T021020/1 and EP/T021063/1. This work is supported in part by the Ajman University Internal Research Grant.

    Publisher Copyright:
    © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.


    • Healthcare
    • RF sensing
    • Wireless sensing

    ASJC Scopus subject areas

    • Computer Networks and Communications


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