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Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring

Innovative technological solutions are required to improve patients’ quality of life and deliver suitable treatment. Healthcare workers may be able to watch patients from a distance using the Internet of Things (IoT) by using big data algorithms to analyze instrument outputs. Therefore, it is essent...

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Autores principales: Maddeh, Mohamed, Hajjej, Fahima, Alazzam, Malik Bader, Otaibi, Shaha Al, Turki, Nazek Al, Ayouni, Sarra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10222067/
https://www.ncbi.nlm.nih.gov/pubmed/37430526
http://dx.doi.org/10.3390/s23104614
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author Maddeh, Mohamed
Hajjej, Fahima
Alazzam, Malik Bader
Otaibi, Shaha Al
Turki, Nazek Al
Ayouni, Sarra
author_facet Maddeh, Mohamed
Hajjej, Fahima
Alazzam, Malik Bader
Otaibi, Shaha Al
Turki, Nazek Al
Ayouni, Sarra
author_sort Maddeh, Mohamed
collection PubMed
description Innovative technological solutions are required to improve patients’ quality of life and deliver suitable treatment. Healthcare workers may be able to watch patients from a distance using the Internet of Things (IoT) by using big data algorithms to analyze instrument outputs. Therefore, it is essential to gather information on use and health problems in order to improve the remedies. To ensure seamless incorporation for use in healthcare institutions, senior communities, or private homes, these technological tools must first and foremost be easy to use and implement. We provide a network cluster-based system known as smart patient room usage in order to achieve this. As a result, nursing staff or caretakers can use it efficiently and swiftly. This work focuses on the exterior unit that makes up a network cluster, a cloud storage mechanism for data processing and storage, as well as a wireless or unique radio frequency send module for data transfer. In this article, a spatio-temporal cluster mapping system is presented and described. This system creates time series data using sense data collected from various clusters. The suggested method is the ideal tool to use in a variety of circumstances to improve medical and healthcare services. The suggested model’s ability to anticipate moving behavior with high precision is its most important feature. The time series graphic displays a regular light movement that continued almost the entire night. The last 12 h’ lowest and highest moving duration numbers were roughly 40% and 50%, respectively. When there is little movement, the model assumes a normal posture. Particularly, the moving duration ranges from 7% to 14%, with an average of 7.0%.
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spelling pubmed-102220672023-05-28 Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring Maddeh, Mohamed Hajjej, Fahima Alazzam, Malik Bader Otaibi, Shaha Al Turki, Nazek Al Ayouni, Sarra Sensors (Basel) Article Innovative technological solutions are required to improve patients’ quality of life and deliver suitable treatment. Healthcare workers may be able to watch patients from a distance using the Internet of Things (IoT) by using big data algorithms to analyze instrument outputs. Therefore, it is essential to gather information on use and health problems in order to improve the remedies. To ensure seamless incorporation for use in healthcare institutions, senior communities, or private homes, these technological tools must first and foremost be easy to use and implement. We provide a network cluster-based system known as smart patient room usage in order to achieve this. As a result, nursing staff or caretakers can use it efficiently and swiftly. This work focuses on the exterior unit that makes up a network cluster, a cloud storage mechanism for data processing and storage, as well as a wireless or unique radio frequency send module for data transfer. In this article, a spatio-temporal cluster mapping system is presented and described. This system creates time series data using sense data collected from various clusters. The suggested method is the ideal tool to use in a variety of circumstances to improve medical and healthcare services. The suggested model’s ability to anticipate moving behavior with high precision is its most important feature. The time series graphic displays a regular light movement that continued almost the entire night. The last 12 h’ lowest and highest moving duration numbers were roughly 40% and 50%, respectively. When there is little movement, the model assumes a normal posture. Particularly, the moving duration ranges from 7% to 14%, with an average of 7.0%. MDPI 2023-05-10 /pmc/articles/PMC10222067/ /pubmed/37430526 http://dx.doi.org/10.3390/s23104614 Text en © 2023 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
Maddeh, Mohamed
Hajjej, Fahima
Alazzam, Malik Bader
Otaibi, Shaha Al
Turki, Nazek Al
Ayouni, Sarra
Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring
title Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring
title_full Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring
title_fullStr Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring
title_full_unstemmed Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring
title_short Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring
title_sort spatio-temporal cluster mapping system in smart beds for patient monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10222067/
https://www.ncbi.nlm.nih.gov/pubmed/37430526
http://dx.doi.org/10.3390/s23104614
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