Wireless Sensing for Human Activity Recognition using USRP

Taylor, W., Shah, S. A. , Dashtipour, K., Le Kernec, J. , Abbasi, Q. H. , Assaleh, K., Arshad, K. and Imran, M. A. (2022) Wireless Sensing for Human Activity Recognition using USRP. In: 16th EAI International Conference on Body Area Networks (EAI BODYNETS 2021), Glasgow, UK, 25-26 Oct 2021, pp. 52-62. ISBN 9783030955922 (doi: 10.1007/978-3-030-95593-9_5)

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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.

Item Type:Conference Proceedings
Additional Information: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.
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Taylor, Mr William and Abbasi, Dr Qammer and Le Kernec, Dr Julien and Dashtipour, Dr Kia and Shah, Mr Syed
Authors: Taylor, W., Shah, S. A., Dashtipour, K., Le Kernec, J., Abbasi, Q. H., Assaleh, K., Arshad, K., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Published Online:11 February 2022
Copyright Holders:Copyright © 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
First Published:First published in 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: 52-62
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
307829Quantum-Inspired Imaging for Remote Monitoring of Health & Disease in Community HealthcareJonathan CooperEngineering and Physical Sciences Research Council (EPSRC)EP/T021020/1ENG - Biomedical Engineering