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Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The...

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Detalles Bibliográficos
Autores principales: R, Latha, P, Vetrivelan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180965/
https://www.ncbi.nlm.nih.gov/pubmed/32290332
http://dx.doi.org/10.3390/s20072153
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author R, Latha
P, Vetrivelan
author_facet R, Latha
P, Vetrivelan
author_sort R, Latha
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description This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.
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spelling pubmed-71809652020-04-30 Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care R, Latha P, Vetrivelan Sensors (Basel) Article This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter. MDPI 2020-04-10 /pmc/articles/PMC7180965/ /pubmed/32290332 http://dx.doi.org/10.3390/s20072153 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
R, Latha
P, Vetrivelan
Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care
title Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care
title_full Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care
title_fullStr Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care
title_full_unstemmed Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care
title_short Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care
title_sort wireless body area network (wban)-based telemedicine for emergency care
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180965/
https://www.ncbi.nlm.nih.gov/pubmed/32290332
http://dx.doi.org/10.3390/s20072153
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