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Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning

To avoid dire situations, the medical sector must develop various methods for quickly and accurately identifying infections in remote regions. The primary goal of the proposed work is to create a wearable device that uses the Internet of Things (IoT) to carry out several monitoring tasks. To decreas...

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Autores principales: Mirza, Olfat M., Mujlid, Hana, Manoharan, Hariprasath, Selvarajan, Shitharth, Srivastava, Gautam, Khan, Muhammad Attique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689082/
https://www.ncbi.nlm.nih.gov/pubmed/36359592
http://dx.doi.org/10.3390/diagnostics12112750
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author Mirza, Olfat M.
Mujlid, Hana
Manoharan, Hariprasath
Selvarajan, Shitharth
Srivastava, Gautam
Khan, Muhammad Attique
author_facet Mirza, Olfat M.
Mujlid, Hana
Manoharan, Hariprasath
Selvarajan, Shitharth
Srivastava, Gautam
Khan, Muhammad Attique
author_sort Mirza, Olfat M.
collection PubMed
description To avoid dire situations, the medical sector must develop various methods for quickly and accurately identifying infections in remote regions. The primary goal of the proposed work is to create a wearable device that uses the Internet of Things (IoT) to carry out several monitoring tasks. To decrease the amount of communication loss as well as the amount of time required to wait before detection and improve detection quality, the designed wearable device is also operated with a multi-objective framework. Additionally, a design method for wearable IoT devices is established, utilizing distinct mathematical approaches to solve these objectives. As a result, the monitored parametric values are saved in a different IoT application platform. Since the proposed study focuses on a multi-objective framework, state design and deep learning (DL) optimization techniques are combined, reducing the complexity of detection in wearable technology. Wearable devices with IoT processes have even been included in current methods. However, a solution cannot be duplicated using mathematical approaches and optimization strategies. Therefore, developed wearable gadgets can be applied to real-time medical applications for fast remote monitoring of an individual. Additionally, the proposed technique is tested in real-time, and an IoT simulation tool is utilized to track the compared experimental results under five different situations. In all of the case studies that were examined, the planned method performs better than the current state-of-the-art methods.
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spelling pubmed-96890822022-11-25 Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning Mirza, Olfat M. Mujlid, Hana Manoharan, Hariprasath Selvarajan, Shitharth Srivastava, Gautam Khan, Muhammad Attique Diagnostics (Basel) Article To avoid dire situations, the medical sector must develop various methods for quickly and accurately identifying infections in remote regions. The primary goal of the proposed work is to create a wearable device that uses the Internet of Things (IoT) to carry out several monitoring tasks. To decrease the amount of communication loss as well as the amount of time required to wait before detection and improve detection quality, the designed wearable device is also operated with a multi-objective framework. Additionally, a design method for wearable IoT devices is established, utilizing distinct mathematical approaches to solve these objectives. As a result, the monitored parametric values are saved in a different IoT application platform. Since the proposed study focuses on a multi-objective framework, state design and deep learning (DL) optimization techniques are combined, reducing the complexity of detection in wearable technology. Wearable devices with IoT processes have even been included in current methods. However, a solution cannot be duplicated using mathematical approaches and optimization strategies. Therefore, developed wearable gadgets can be applied to real-time medical applications for fast remote monitoring of an individual. Additionally, the proposed technique is tested in real-time, and an IoT simulation tool is utilized to track the compared experimental results under five different situations. In all of the case studies that were examined, the planned method performs better than the current state-of-the-art methods. MDPI 2022-11-10 /pmc/articles/PMC9689082/ /pubmed/36359592 http://dx.doi.org/10.3390/diagnostics12112750 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
Mirza, Olfat M.
Mujlid, Hana
Manoharan, Hariprasath
Selvarajan, Shitharth
Srivastava, Gautam
Khan, Muhammad Attique
Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning
title Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning
title_full Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning
title_fullStr Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning
title_full_unstemmed Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning
title_short Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning
title_sort mathematical framework for wearable devices in the internet of things using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689082/
https://www.ncbi.nlm.nih.gov/pubmed/36359592
http://dx.doi.org/10.3390/diagnostics12112750
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