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Anomaly detection using smart shirt and machine learning: a systematic review

2022Conference paper Restricted access
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dc.contributor.authorNunes, Eduardo
dc.contributor.authorBarbosa, José
dc.contributor.authorAlves, Paulo
dc.contributor.authorFranco, Tiago
dc.contributor.authorSilva, Alfredo
dc.date.accessioned2023-03-16T09:47:37Z
dc.date.available2023-03-16T09:47:37Z
dc.date.issued2022
dc.description.abstractIn recent years, the popularity and use of Artificial Intelligence (AI) and significant investments in the Internet of Medical Things (IoMT) will be common to use products such as smart socks, smart pants, and smart shirts. These products are known as Smart Textile or E-textile, which can monitor and collect signals that our body emits. These signals allow it to extract anomalous components using Machine Learning (ML) techniques that play an essential role in this area. This study presents a Systematic Literature Review (SLR) on Anomaly Detection using ML techniques in Smart Shirt. The objectives of the SLR are: (i) to identify what type of anomaly the smart shirt can detect; (ii) what ML techniques are in use; (iii) which datasets are in use; (iv) identify smart shirt or signal acquisition devices worn in the chest region; (v) list the performance metrics used to evaluate the ML model; (vi) the results of the techniques in general; (vii) types of ML algorithms are being applied. The SLR selected eleven primary studies published between January/2017-May/2022. The results showed that six anomalies were identified, with the Fall anomaly being the most cited. The Support Vector Machines (SVM) algorithm is the most used. Most of the primary studies used public or private datasets. The Hexoskin smart shirt was most cited. The most used metric performance was accuracy. Almost all primary studies presented a result above 90%, and all primary studies used the Supervisioned type of ML.pt_PT
dc.description.sponsorshipThis work has been supported by Nanomateriais aplicados na reabilitação muscular de IDosos com recurso à Inteligência Artificial (46985)
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationNunes, Eduardo; Barbosa, José; Alves, Paulo; Franco, Tiago; Silva, Alfredo (2022). Anomaly detection using smart shirt and machine learning: a systematic review. In OL2A 2022. Póvoa de Varzimpt_PT
dc.identifier.doi10.1007/978-3-031-23236-7_33pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/27769
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMachine learningpt_PT
dc.subjectAnomaly detectionpt_PT
dc.subjectSmart shirtpt_PT
dc.subjectSmart textilept_PT
dc.subjectSystematic reviewpt_PT
dc.titleAnomaly detection using smart shirt and machine learning: a systematic reviewpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferencePlacePóvoa de Varzimpt_PT
oaire.citation.titleOL2A 2022pt_PT
oaire.citation.volume1754pt_PT
person.familyNameBarbosa
person.familyNameAlves
person.familyNameFranco
person.givenNameJosé
person.givenNamePaulo
person.givenNameTiago
person.identifier609187
person.identifierUAMm8moAAAAJ&hl
person.identifier.ciencia-id021B-4191-D8A5
person.identifier.ciencia-idC319-FC42-5B6B
person.identifier.ciencia-id7F19-C649-5DD9
person.identifier.orcid0000-0003-3151-6686
person.identifier.orcid0000-0002-0100-8691
person.identifier.orcid0000-0001-8574-4380
person.identifier.ridA-5468-2011
person.identifier.scopus-author-id48360905400
person.identifier.scopus-author-id55834442100
person.identifier.scopus-author-id57223608236
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication0c76a063-ff3c-4db3-b6e7-36885020b399
relation.isAuthorOfPublication43d3b0cd-8fd9-4194-a9df-9cca66f8726b
relation.isAuthorOfPublication77169a8f-77ce-4994-8310-3e3710e07520
relation.isAuthorOfPublication.latestForDiscovery77169a8f-77ce-4994-8310-3e3710e07520

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