Data Fusion in Internet of Things
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.6/10222 |
Resumo: | This dissertation reviews Internet of Things concepts and implementations, state-of-the-art technology with practical examples, as well as data fusion methods applied in different problems. The purpose of this study is to review different data fusion methods and develop a system to provide recognition of human activity that can be applied in day care homes and in hospitals to monitor patients. The system’s objective is to study human activity recognition based on the data recovered by sensors like accelerometers and gyroscopes. In order to transform this data to useful information and practical results to monitoring patients with accuracy and high performance, two different neural networks were implemented. To conclude, the results from the two different neural networks are compared to each other and compared with systems from other authors. It is hoped this study will inform other authors and developers about the performance of neural networks when managing human activity recognition systems. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Data Fusion in Internet of ThingsData FusionDeep LearningHealth-CareHuman Activity RecognitionInternet of ThingsNeural NetworksSmart HomeDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaThis dissertation reviews Internet of Things concepts and implementations, state-of-the-art technology with practical examples, as well as data fusion methods applied in different problems. The purpose of this study is to review different data fusion methods and develop a system to provide recognition of human activity that can be applied in day care homes and in hospitals to monitor patients. The system’s objective is to study human activity recognition based on the data recovered by sensors like accelerometers and gyroscopes. In order to transform this data to useful information and practical results to monitoring patients with accuracy and high performance, two different neural networks were implemented. To conclude, the results from the two different neural networks are compared to each other and compared with systems from other authors. It is hoped this study will inform other authors and developers about the performance of neural networks when managing human activity recognition systems.Pombo, Nuno Gonçalo Coelho CostaSantos, Nuno Manuel Garcia dosMerilampi, SariuBibliorumMendes, Tiago Nobre de Albuquerque2020-03-25T14:33:35Z2019-07-302019-06-242019-07-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.6/10222TID:202365255enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-15T09:51:40Zoai:ubibliorum.ubi.pt:10400.6/10222Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:50:14.271797Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Data Fusion in Internet of Things |
title |
Data Fusion in Internet of Things |
spellingShingle |
Data Fusion in Internet of Things Mendes, Tiago Nobre de Albuquerque Data Fusion Deep Learning Health-Care Human Activity Recognition Internet of Things Neural Networks Smart Home Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
title_short |
Data Fusion in Internet of Things |
title_full |
Data Fusion in Internet of Things |
title_fullStr |
Data Fusion in Internet of Things |
title_full_unstemmed |
Data Fusion in Internet of Things |
title_sort |
Data Fusion in Internet of Things |
author |
Mendes, Tiago Nobre de Albuquerque |
author_facet |
Mendes, Tiago Nobre de Albuquerque |
author_role |
author |
dc.contributor.none.fl_str_mv |
Pombo, Nuno Gonçalo Coelho Costa Santos, Nuno Manuel Garcia dos Merilampi, Sari uBibliorum |
dc.contributor.author.fl_str_mv |
Mendes, Tiago Nobre de Albuquerque |
dc.subject.por.fl_str_mv |
Data Fusion Deep Learning Health-Care Human Activity Recognition Internet of Things Neural Networks Smart Home Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
topic |
Data Fusion Deep Learning Health-Care Human Activity Recognition Internet of Things Neural Networks Smart Home Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
description |
This dissertation reviews Internet of Things concepts and implementations, state-of-the-art technology with practical examples, as well as data fusion methods applied in different problems. The purpose of this study is to review different data fusion methods and develop a system to provide recognition of human activity that can be applied in day care homes and in hospitals to monitor patients. The system’s objective is to study human activity recognition based on the data recovered by sensors like accelerometers and gyroscopes. In order to transform this data to useful information and practical results to monitoring patients with accuracy and high performance, two different neural networks were implemented. To conclude, the results from the two different neural networks are compared to each other and compared with systems from other authors. It is hoped this study will inform other authors and developers about the performance of neural networks when managing human activity recognition systems. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07-30 2019-06-24 2019-07-30T00:00:00Z 2020-03-25T14:33:35Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.6/10222 TID:202365255 |
url |
http://hdl.handle.net/10400.6/10222 |
identifier_str_mv |
TID:202365255 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136392990687232 |