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Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data

2020-01Journal 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.date.accessioned2020-01-24T15:37:42Z
dc.date.available2020-01-24T15:37:42Z
dc.date.issued2020-01
dc.description.abstractUsing the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper focuses on the review of the studies that use the AdaBoost method with the sensors available in mobile devices. This research identified the research works written in English about the recognition of daily activities and environment recognition using the AdaBoost method with the data obtained from the sensors available in mobile devices that were published between 2012 and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched databases. The results proved the reliability of the method for daily activities and environment recognition, highlighting the use of several features, including the mean, standard deviation, pitch, roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than 80% in recognition of daily activities and environments with the Adaboost method.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/electronics9010192pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8727
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationInstituto de Telecomunicações
dc.subjectDaily activities recognitionpt_PT
dc.subjectEnsemble learningpt_PT
dc.subjectEnsemble classifierspt_PT
dc.subjectEnvironmentspt_PT
dc.subjectMobile devicespt_PT
dc.subjectSensorspt_PT
dc.subjectSystematic reviewpt_PT
dc.titleIdentification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Datapt_PT
dc.title.alternativeA Systematic Reviewpt_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.startPage192pt_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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