Multiple participants’ discrete activity recognition in a well-controlled environment using universal software radio peripheral wireless sensing

Saeed, U., Shah, S. Y., Shah, S. A. , Liu, H., Alotaibi, A. A., Althobaiti, T., Ramzan, N., Jan, S. U., Ahmad, J. and Abbasi, Q. H. (2022) Multiple participants’ discrete activity recognition in a well-controlled environment using universal software radio peripheral wireless sensing. Sensors, 22(3), 809. (doi: 10.3390/s22030809) (PMID:5161555) (PMCID:PMC8838354)

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Abstract

Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is for smart care centres or homes. In this paper, a smart monitoring system for different human activities is proposed based on radio-frequency sensing integrated with ensemble machine learning models. The ensemble technique can recognise a wide range of activity based on alterations in the wireless signal’s Channel State Information (CSI). The proposed system operates at 3.75 GHz, and up to four subjects participated in the experimental study in order to acquire data on sixteen distinct daily living activities: sitting, standing, and walking. The proposed methodology merges subject count and performed activities, resulting in occupancy count and activity performed being recognised at the same time. To capture alterations owing to concurrent multi-subject motions, the CSI amplitudes collected from 51 subcarriers of the wireless signals were processed and merged. To distinguish multi-subject activity, a machine learning model based on an ensemble learning technique was designed and trained using the acquired CSI data. For maximum activity classes, the proposed approach attained a high average accuracy of up to 98%. The presented system has the ability to fulfil prospective health activity monitoring demands and is a viable solution towards well-being tracking.

Item Type:Articles
Additional Information:This work is supported in parts by the Engineering and Physical Sciences Research Council (EPSRC): EP/R511705/1 and EP/T021063/1. The authors extend their appreciation to the Deputyship for Research Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number IF-2020-NBU-201 and in part by the Taif University, Taif, Saudi Arabia, through the Taif University Research Grant under Project TURSP-2020/277.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Abbasi, Professor Qammer and Shah, Mr Syed
Creator Roles:
Shah, S. A.Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Supervision
Abbasi, Q. H.Conceptualization, Formal analysis, Investigation
Authors: Saeed, U., Shah, S. Y., Shah, S. A., Liu, H., Alotaibi, A. A., Althobaiti, T., Ramzan, N., Jan, S. U., Ahmad, J., and Abbasi, Q. H.
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
Journal Name:Sensors
Publisher:MDPI
ISSN:1424-8220
ISSN (Online):1424-8220
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Sensors 22(3):809
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304896EPSRC-IAA: Early Stage Commercialisation of a PET Imaging Agent for the Detection of Cardiovascular Disease and CancerAndrew SutherlandEngineering and Physical Sciences Research Council (EPSRC)EP/R511705/1Chemistry
307826COG-MHEAR: Towards cognitiveky-inspired 5G-IoT enabled, multi-modal Hearing AidsQammer H AbbasiEngineering and Physical Sciences Research Council (EPSRC)EP/T021063/1ENG - Systems Power & Energy