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Activities of Daily Living and Environment Recognition Using Mobile Devices

2020Journal article Open access
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dc.contributor.authorFerreira, José M.
dc.contributor.authorPires, Ivan
dc.contributor.authorMarques, Gonçalo
dc.contributor.authorGarcia, Nuno M.
dc.contributor.authorZdravevski, Eftim
dc.contributor.authorLameski, Petre
dc.contributor.authorFlórez-Revuelta, Francisco
dc.contributor.authorSpinsante, Susanna
dc.contributor.authorXu, Lina
dc.date.accessioned2020-01-24T15:45:23Z
dc.date.available2020-01-24T15:45:23Z
dc.date.issued2020
dc.description.abstractThe recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/electronics9010180pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8728
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationInstituto de Telecomunicações
dc.subjectActivities of daily livingpt_PT
dc.subjectAdaBoostpt_PT
dc.subjectMobile devicespt_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectDeep neural networkspt_PT
dc.titleActivities of Daily Living and Environment Recognition Using Mobile Devicespt_PT
dc.title.alternativeA Comparative Studypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.citation.issue1pt_PT
oaire.citation.startPage180pt_PT
oaire.citation.titleElectronicspt_PT
oaire.citation.volume9pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSerrano Pires
person.familyNameMarques
person.familyNameGarcia dos Santos
person.familyNameZdravevski
person.familyNameLameski
person.familyNameFlórez-Revuelta
person.familyNameSpinsante
person.givenNameIvan Miguel
person.givenNameGonçalo
person.givenNameNuno Manuel
person.givenNameEftim
person.givenNamePetre
person.givenNameFrancisco
person.givenNameSusanna
person.identifier-6iey0oAAAAJ
person.identifier.ciencia-id211D-8B3D-0131
person.identifier.ciencia-idE719-0DEC-9751
person.identifier.orcid0000-0002-3394-6762
person.identifier.orcid0000-0001-5834-6571
person.identifier.orcid0000-0002-3195-3168
person.identifier.orcid0000-0001-7664-0168
person.identifier.orcid0000-0002-5336-1796
person.identifier.orcid0000-0002-3391-711X
person.identifier.orcid0000-0002-7323-4030
person.identifier.ridN-1805-2018
person.identifier.ridK-5276-2014
person.identifier.ridJ-3370-2013
person.identifier.ridP-5437-2014
person.identifier.scopus-author-id56715367700
person.identifier.scopus-author-id13106226300
person.identifier.scopus-author-id6506113067
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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