5G-enabled contactless multi-user presence and activity detection for independent assisted living

Abstract Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart h...

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Autores principales:	Aboajeila Milad Ashleibta, Ahmad Taha, Muhammad Aurangzeb Khan, William Taylor, Ahsen Tahir, Ahmed Zoha, Qammer H. Abbasi, Muhammad Ali Imran
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7d6ba7febccb4a698a4aebec3814f90d
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spelling	oai:doaj.org-article:7d6ba7febccb4a698a4aebec3814f90d2021-12-02T19:10:43Z5G-enabled contactless multi-user presence and activity detection for independent assisted living10.1038/s41598-021-96689-72045-2322https://doaj.org/article/7d6ba7febccb4a698a4aebec3814f90d2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96689-7https://doaj.org/toc/2045-2322Abstract Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors’ knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being.Aboajeila Milad AshleibtaAhmad TahaMuhammad Aurangzeb KhanWilliam TaylorAhsen TahirAhmed ZohaQammer H. AbbasiMuhammad Ali ImranNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aboajeila Milad Ashleibta Ahmad Taha Muhammad Aurangzeb Khan William Taylor Ahsen Tahir Ahmed Zoha Qammer H. Abbasi Muhammad Ali Imran 5G-enabled contactless multi-user presence and activity detection for independent assisted living
description	Abstract Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors’ knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being.
format	article
author	Aboajeila Milad Ashleibta Ahmad Taha Muhammad Aurangzeb Khan William Taylor Ahsen Tahir Ahmed Zoha Qammer H. Abbasi Muhammad Ali Imran
author_facet	Aboajeila Milad Ashleibta Ahmad Taha Muhammad Aurangzeb Khan William Taylor Ahsen Tahir Ahmed Zoha Qammer H. Abbasi Muhammad Ali Imran
author_sort	Aboajeila Milad Ashleibta
title	5G-enabled contactless multi-user presence and activity detection for independent assisted living
title_short	5G-enabled contactless multi-user presence and activity detection for independent assisted living
title_full	5G-enabled contactless multi-user presence and activity detection for independent assisted living
title_fullStr	5G-enabled contactless multi-user presence and activity detection for independent assisted living
title_full_unstemmed	5G-enabled contactless multi-user presence and activity detection for independent assisted living
title_sort	5g-enabled contactless multi-user presence and activity detection for independent assisted living
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7d6ba7febccb4a698a4aebec3814f90d
work_keys_str_mv	AT aboajeilamiladashleibta 5genabledcontactlessmultiuserpresenceandactivitydetectionforindependentassistedliving AT ahmadtaha 5genabledcontactlessmultiuserpresenceandactivitydetectionforindependentassistedliving AT muhammadaurangzebkhan 5genabledcontactlessmultiuserpresenceandactivitydetectionforindependentassistedliving AT williamtaylor 5genabledcontactlessmultiuserpresenceandactivitydetectionforindependentassistedliving AT ahsentahir 5genabledcontactlessmultiuserpresenceandactivitydetectionforindependentassistedliving AT ahmedzoha 5genabledcontactlessmultiuserpresenceandactivitydetectionforindependentassistedliving AT qammerhabbasi 5genabledcontactlessmultiuserpresenceandactivitydetectionforindependentassistedliving AT muhammadaliimran 5genabledcontactlessmultiuserpresenceandactivitydetectionforindependentassistedliving
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5G-enabled contactless multi-user presence and activity detection for independent assisted living

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