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Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing

Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitore...

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Autores principales: Mohana, J., Yakkala, Bhaskarrao, Vimalnath, S., Benson Mansingh, P. M., Yuvaraj, N., Srihari, K., Sasikala, G., Mahalakshmi, V., Yasir Abdullah, R., Sundramurthy, Venkatesa Prabhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808223/
https://www.ncbi.nlm.nih.gov/pubmed/35126905
http://dx.doi.org/10.1155/2022/1892123
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author Mohana, J.
Yakkala, Bhaskarrao
Vimalnath, S.
Benson Mansingh, P. M.
Yuvaraj, N.
Srihari, K.
Sasikala, G.
Mahalakshmi, V.
Yasir Abdullah, R.
Sundramurthy, Venkatesa Prabhu
author_facet Mohana, J.
Yakkala, Bhaskarrao
Vimalnath, S.
Benson Mansingh, P. M.
Yuvaraj, N.
Srihari, K.
Sasikala, G.
Mahalakshmi, V.
Yasir Abdullah, R.
Sundramurthy, Venkatesa Prabhu
author_sort Mohana, J.
collection PubMed
description Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.
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spelling pubmed-88082232022-02-03 Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing Mohana, J. Yakkala, Bhaskarrao Vimalnath, S. Benson Mansingh, P. M. Yuvaraj, N. Srihari, K. Sasikala, G. Mahalakshmi, V. Yasir Abdullah, R. Sundramurthy, Venkatesa Prabhu J Healthc Eng Research Article Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets. Hindawi 2022-01-25 /pmc/articles/PMC8808223/ /pubmed/35126905 http://dx.doi.org/10.1155/2022/1892123 Text en Copyright © 2022 J. Mohana et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Mohana, J.
Yakkala, Bhaskarrao
Vimalnath, S.
Benson Mansingh, P. M.
Yuvaraj, N.
Srihari, K.
Sasikala, G.
Mahalakshmi, V.
Yasir Abdullah, R.
Sundramurthy, Venkatesa Prabhu
Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing
title Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing
title_full Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing
title_fullStr Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing
title_full_unstemmed Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing
title_short Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing
title_sort application of internet of things on the healthcare field using convolutional neural network processing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808223/
https://www.ncbi.nlm.nih.gov/pubmed/35126905
http://dx.doi.org/10.1155/2022/1892123
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