Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Manogaran, Gunasekaran | - |
dc.contributor.author | Shakeel, P. Mohamed | - |
dc.contributor.author | Fouad, H. | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.contributor.author | Baskar, S. | - |
dc.contributor.author | Chilamkurti, Naveen | - |
dc.contributor.author | Sundarasekar, Revathi | - |
dc.date.accessioned | 2021-08-11T09:43:34Z | - |
dc.date.available | 2021-08-11T09:43:34Z | - |
dc.date.issued | 2019-07-01 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/4394 | - |
dc.description.abstract | According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
dc.title | Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/s19133030 | - |
dc.identifier.scopusid | 2-s2.0-85070464464 | - |
dc.identifier.wosid | 000477045000188 | - |
dc.identifier.bibliographicCitation | Sensors, v.19, no.13 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 19 | - |
dc.citation.number | 13 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | HEALTH | - |
dc.subject.keywordPlus | INTERNET | - |
dc.subject.keywordAuthor | multi access physical monitoring system | - |
dc.subject.keywordAuthor | multimedia technology | - |
dc.subject.keywordAuthor | edge computing | - |
dc.subject.keywordAuthor | Bayesian neural network | - |
dc.subject.keywordAuthor | smart-log patch | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.