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Identification of diseases based on the use of inertial sensors: a systematic review

2020Journal article Open access
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dc.contributor.authorPonciano, Vasco Rafael Gaspar
dc.contributor.authorPires, Ivan M.
dc.contributor.authorRibeiro, Fernando Reinaldo
dc.contributor.authorMarques, Gonçalo Santos
dc.contributor.authorVillasana, María Vanessa
dc.contributor.authorGarcia, Nuno M.
dc.contributor.authorZdravevski, Eftim
dc.contributor.authorSpinsante, Susanna
dc.date.accessioned2020-05-12T11:42:44Z
dc.date.available2020-05-12T11:42:44Z
dc.date.issued2020
dc.description.abstractInertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer for the automatic recognition of different diseases, and it may powerful the different treatments with the use of less invasive and painful techniques for patients. This paper is focused in the systematic review of the studies available in the literature for the automatic recognition of different diseases with accelerometer sensors. The disease that is the most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implements for the recognition of Parkinson’s disease reported an accuracy of 94%. Other diseases are recognized in less number that will be subject of further analysis in the future.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPONCIANO, V. [et al.] (2020) - Identification of diseases based on the use of inertial sensors: a systematic review. Electronics. ISSN 2079-9292. Vol 9, nº 5, p. 1-17. Doi: 10.3390/electronics9050778pt_PT
dc.identifier.doi10.3390/electronics9050778pt_PT
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/10400.11/7082
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMultidisciplinary Digital Publishing Institutept_PT
dc.relationUIDB/EEA/50008/2020pt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2079-9292/9/5/778pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectWearable electronic devicespt_PT
dc.subjectDiseasespt_PT
dc.subjectMonitoringpt_PT
dc.subjectAutomatic identificationpt_PT
dc.titleIdentification of diseases based on the use of inertial sensors: a systematic reviewpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue778pt_PT
oaire.citation.titleElectronics. Special Issue Electronic Solutions for Artificial Intelligence Healthcarept_PT
oaire.citation.volume9pt_PT
person.familyNameSerrano Pires
person.familyNameReinaldo Silva Garcia Ribeiro
person.familyNameSantos Marques
person.familyNameGarcia dos Santos
person.givenNameIvan Miguel
person.givenNameFernando
person.givenNameGonçalo Miguel
person.givenNameNuno Manuel
person.identifier.ciencia-id211D-8B3D-0131
person.identifier.ciencia-id7B1C-D761-291D
person.identifier.ciencia-idD410-69E4-F1AA
person.identifier.ciencia-idE719-0DEC-9751
person.identifier.orcid0000-0002-3394-6762
person.identifier.orcid0000-0002-1225-3844
person.identifier.orcid0000-0001-5834-6571
person.identifier.orcid0000-0002-3195-3168
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication53c92dfa-27be-4305-9b32-f3cf2e36a4f1
relation.isAuthorOfPublication165761b1-f958-4c13-b53f-ef0a4dde1d97
relation.isAuthorOfPublication6a74cfba-4e39-48b2-99fa-1dfe2e0cd7a8
relation.isAuthorOfPublicationc3c2a619-5f2a-42ae-8ee2-f21b9c42a33a
relation.isAuthorOfPublication.latestForDiscovery6a74cfba-4e39-48b2-99fa-1dfe2e0cd7a8

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