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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