About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings

Research Article

Wireless Sensing for Human Activity Recognition Using USRP

Download(Requires a free EAI acccount)
4 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-030-95593-9_5,
        author={William Taylor and Syed Aziz Shah and Kia Dashtipour and Julien Le Kernec and Qammer H. Abbasi and Khaled Assaleh and Kamran Arshad and Muhammad Ali Imran},
        title={Wireless Sensing for Human Activity Recognition Using USRP},
        proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings},
        proceedings_a={BODYNETS},
        year={2022},
        month={2},
        keywords={Wireless sensing Healthcare RF sensing},
        doi={10.1007/978-3-030-95593-9_5}
    }
    
  • William Taylor
    Syed Aziz Shah
    Kia Dashtipour
    Julien Le Kernec
    Qammer H. Abbasi
    Khaled Assaleh
    Kamran Arshad
    Muhammad Ali Imran
    Year: 2022
    Wireless Sensing for Human Activity Recognition Using USRP
    BODYNETS
    Springer
    DOI: 10.1007/978-3-030-95593-9_5
William Taylor1,*, Syed Aziz Shah2, Kia Dashtipour1, Julien Le Kernec1, Qammer H. Abbasi1, Khaled Assaleh3, Kamran Arshad3, Muhammad Ali Imran1
  • 1: James Watt School of Engineering, University of Glasgow
  • 2: Centre for Intelligent Healthcare, Coventry University
  • 3: Faculty of Engineering and IT, Ajman University
*Contact email: w.taylor.2@research.gla.ac.uk

Abstract

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.

Keywords
Wireless sensing Healthcare RF sensing
Published
2022-02-11
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-95593-9_5
Copyright © 2021–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL