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Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

2019Journal article Open access
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dc.contributor.authorPires, Ivan
dc.contributor.authorMarques, Gonçalo
dc.contributor.authorGarcia, Nuno M.
dc.contributor.authorPombo, Nuno
dc.contributor.authorFlórez-Revuelta, Francisco
dc.contributor.authorSpinsante, Susanna
dc.contributor.authorTeixeira, Maria Canavarro
dc.contributor.authorZdravevski, Eftim
dc.date.accessioned2020-01-14T14:33:30Z
dc.date.available2020-01-14T14:33:30Z
dc.date.issued2019
dc.description.abstractThe identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/electronics8121499pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8255
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectActivities of Daily Living (ADL)pt_PT
dc.subjectData fusionpt_PT
dc.subjectEnvironmentspt_PT
dc.subjectFeature extractionpt_PT
dc.subjectPattern recognitionpt_PT
dc.subjectSensorspt_PT
dc.titleRecognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devicespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue12pt_PT
oaire.citation.startPage1499pt_PT
oaire.citation.titleElectronicspt_PT
oaire.citation.volume8pt_PT
person.familyNameSerrano Pires
person.familyNameMarques
person.familyNameGarcia dos Santos
person.familyNamePombo
person.familyNameSpinsante
person.familyNameZdravevski
person.givenNameIvan Miguel
person.givenNameGonçalo
person.givenNameNuno Manuel
person.givenNameNuno
person.givenNameSusanna
person.givenNameEftim
person.identifier-6iey0oAAAAJ
person.identifier.ciencia-id211D-8B3D-0131
person.identifier.ciencia-idE719-0DEC-9751
person.identifier.ciencia-id0F16-A18D-96BA
person.identifier.orcid0000-0002-3394-6762
person.identifier.orcid0000-0001-5834-6571
person.identifier.orcid0000-0002-3195-3168
person.identifier.orcid0000-0001-7797-8849
person.identifier.orcid0000-0002-7323-4030
person.identifier.orcid0000-0001-7664-0168
person.identifier.ridN-1805-2018
person.identifier.ridP-5437-2014
person.identifier.ridK-5276-2014
person.identifier.scopus-author-id56715367700
person.identifier.scopus-author-id55389546100
person.identifier.scopus-author-id6506113067
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationea9158cf-25f0-47ca-9e3a-6283aa28e65c
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relation.isAuthorOfPublication.latestForDiscovery3648e9b2-25ee-4d13-9af2-4addc30dae7c

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