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Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing

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 sm...

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Autores principales: Saeed, Umer, Yaseen Shah, Syed, Aziz Shah, Syed, Liu, Haipeng, Alhumaidi Alotaibi, Abdullah, Althobaiti, Turke, Ramzan, Naeem, Ullah Jan, Sana, Ahmad, Jawad, Abbasi, Qammer H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838354/
https://www.ncbi.nlm.nih.gov/pubmed/35161555
http://dx.doi.org/10.3390/s22030809
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author Saeed, Umer
Yaseen Shah, Syed
Aziz Shah, Syed
Liu, Haipeng
Alhumaidi Alotaibi, Abdullah
Althobaiti, Turke
Ramzan, Naeem
Ullah Jan, Sana
Ahmad, Jawad
Abbasi, Qammer H.
author_facet Saeed, Umer
Yaseen Shah, Syed
Aziz Shah, Syed
Liu, Haipeng
Alhumaidi Alotaibi, Abdullah
Althobaiti, Turke
Ramzan, Naeem
Ullah Jan, Sana
Ahmad, Jawad
Abbasi, Qammer H.
author_sort Saeed, Umer
collection PubMed
description 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.
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spelling pubmed-88383542022-02-13 Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing Saeed, Umer Yaseen Shah, Syed Aziz Shah, Syed Liu, Haipeng Alhumaidi Alotaibi, Abdullah Althobaiti, Turke Ramzan, Naeem Ullah Jan, Sana Ahmad, Jawad Abbasi, Qammer H. Sensors (Basel) Article 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. MDPI 2022-01-21 /pmc/articles/PMC8838354/ /pubmed/35161555 http://dx.doi.org/10.3390/s22030809 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Saeed, Umer
Yaseen Shah, Syed
Aziz Shah, Syed
Liu, Haipeng
Alhumaidi Alotaibi, Abdullah
Althobaiti, Turke
Ramzan, Naeem
Ullah Jan, Sana
Ahmad, Jawad
Abbasi, Qammer H.
Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing
title Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing
title_full Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing
title_fullStr Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing
title_full_unstemmed Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing
title_short Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing
title_sort multiple participants’ discrete activity recognition in a well-controlled environment using universal software radio peripheral wireless sensing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838354/
https://www.ncbi.nlm.nih.gov/pubmed/35161555
http://dx.doi.org/10.3390/s22030809
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