Human activity recognition for indoor localization using smartphone inertial sensors

Detalhes bibliográficos
Autor(a) principal: Moreira, Dinis
Data de Publicação: 2021
Outros Autores: Barandas, Marília, Alves, Pedro, Santos, Ricardo, Leonardo, Ricardo, Vieira, Pedro, Gamboa, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/132797
Resumo: POCI-01-0247-FEDER-033479
id RCAP_6a8b41bf193386907cf523230638a4e7
oai_identifier_str oai:run.unl.pt:10362/132797
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Human activity recognition for indoor localization using smartphone inertial sensorsDeep learningHuman activity recognitionIndoor locationInertial sensorsSmartphoneAnalytical ChemistryInformation SystemsAtomic and Molecular Physics, and OpticsBiochemistryInstrumentationElectrical and Electronic EngineeringPOCI-01-0247-FEDER-033479With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users’ performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users’ current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.DF – Departamento de FísicaLIBPhys-UNLRUNMoreira, DinisBarandas, MaríliaAlves, PedroSantos, RicardoLeonardo, RicardoVieira, PedroGamboa, Hugo2022-02-12T23:27:25Z2021-09-212021-09-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/132797eng1424-8220PURE: 36763436https://doi.org/10.3390/s21186316info: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:RCAAP2024-03-11T05:11:28Zoai:run.unl.pt:10362/132797Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:36.133117Repositório 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 Human activity recognition for indoor localization using smartphone inertial sensors
title Human activity recognition for indoor localization using smartphone inertial sensors
spellingShingle Human activity recognition for indoor localization using smartphone inertial sensors
Moreira, Dinis
Deep learning
Human activity recognition
Indoor location
Inertial sensors
Smartphone
Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
title_short Human activity recognition for indoor localization using smartphone inertial sensors
title_full Human activity recognition for indoor localization using smartphone inertial sensors
title_fullStr Human activity recognition for indoor localization using smartphone inertial sensors
title_full_unstemmed Human activity recognition for indoor localization using smartphone inertial sensors
title_sort Human activity recognition for indoor localization using smartphone inertial sensors
author Moreira, Dinis
author_facet Moreira, Dinis
Barandas, Marília
Alves, Pedro
Santos, Ricardo
Leonardo, Ricardo
Vieira, Pedro
Gamboa, Hugo
author_role author
author2 Barandas, Marília
Alves, Pedro
Santos, Ricardo
Leonardo, Ricardo
Vieira, Pedro
Gamboa, Hugo
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv DF – Departamento de Física
LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Moreira, Dinis
Barandas, Marília
Alves, Pedro
Santos, Ricardo
Leonardo, Ricardo
Vieira, Pedro
Gamboa, Hugo
dc.subject.por.fl_str_mv Deep learning
Human activity recognition
Indoor location
Inertial sensors
Smartphone
Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
topic Deep learning
Human activity recognition
Indoor location
Inertial sensors
Smartphone
Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
description POCI-01-0247-FEDER-033479
publishDate 2021
dc.date.none.fl_str_mv 2021-09-21
2021-09-21T00:00:00Z
2022-02-12T23:27:25Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/132797
url http://hdl.handle.net/10362/132797
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1424-8220
PURE: 36763436
https://doi.org/10.3390/s21186316
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
_version_ 1799138078737039360

Registros relacionados