IoT Based Smart Health Monitoring with CNN Using Edge Computing
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Vimal, S. | - |
dc.contributor.author | Robinson, Y. Harold | - |
dc.contributor.author | Kadry, Seifedine | - |
dc.contributor.author | Hoang Viet Long | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.date.accessioned | 2021-08-11T08:31:11Z | - |
dc.date.available | 2021-08-11T08:31:11Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1607-9264 | - |
dc.identifier.issn | 2079-4029 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2193 | - |
dc.description.abstract | In the last years, healthcare monitoring has followed a great upwards grown compared to the past decade with the intrusion of the Internet of Things (IoT). The IoT based health paradigm has a vibrant role in health care services to enhance data processing and data prediction. Fall accidents are common in the elderly persons. IoT with Artificial Intelligence (AI) provides a major paradigm to predict the human control, to analyze the cause of human tendency. Fall detection is a major prevailing need with the elderly person, and in order to mitigate this problem an AI based deep Convolutional neural network is proposed to analyze the cause of falling. The deep convolution neural network has been proposed together with the fog and edge computing to analyze the health monitoring tasks. This work analyses the architecture and motions of the persons for fall detection with the sensor nodes. An experimental study is carried out with a benchmark dataset and the higher accuracy in the classification is obtained with this proposal in the simulation results. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | National Dong Hwa University | - |
dc.title | IoT Based Smart Health Monitoring with CNN Using Edge Computing | - |
dc.type | Article | - |
dc.publisher.location | 대만 | - |
dc.identifier.doi | 10.3966/160792642021012201017 | - |
dc.identifier.scopusid | 2-s2.0-85105027385 | - |
dc.identifier.wosid | 000635593500017 | - |
dc.identifier.bibliographicCitation | Journal of Internet Technology, v.22, no.1, pp 173 - 185 | - |
dc.citation.title | Journal of Internet Technology | - |
dc.citation.volume | 22 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 173 | - |
dc.citation.endPage | 185 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | FALL DETECTION | - |
dc.subject.keywordPlus | REAL-TIME | - |
dc.subject.keywordPlus | ACTIVITY RECOGNITION | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordAuthor | IoT | - |
dc.subject.keywordAuthor | Health care | - |
dc.subject.keywordAuthor | AI | - |
dc.subject.keywordAuthor | Convolution neural network | - |
dc.subject.keywordAuthor | Fall detection | - |
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.