Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70136
DC FieldValueLanguage
dc.contributor.authorSteinmetzer, Tobias-
dc.contributor.authorMaasch, Michele-
dc.contributor.authorBönninger, Ingrid-
dc.contributor.authorTravieso González, Carlos Manuel-
dc.date.accessioned2020-02-05T12:52:37Z-
dc.date.available2020-02-05T12:52:37Z-
dc.date.issued2019-
dc.identifier.issn2079-9292-
dc.identifier.otherScopus-
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/70136-
dc.description.abstractDue to increasing life expectancy, the number of age-related diseases with motor dysfunctions (MD) such as Parkinson’s disease (PD) is also increasing. The assessment of MD is visual and therefore subjective. For this reason, many researchers are working on an objective evaluation. Most of the research on gait analysis deals with the analysis of leg movement. The analysis of arm movement is also important for the assessment of gait disorders. This work deals with the analysis of the arm swing by using wearable inertial sensors. A total of 250 records of 39 different subjects were used for this task. Fifteen subjects of this group had motor dysfunctions (MD). The subjects had to perform the standardized Timed Up and Go (TUG) test to ensure that the recordings were comparable. The data were classified by using the wavelet transformation, a convolutional neural network (CNN), and weight voting. During the classification, single signals, as well as signal combinations were observed. We were able to detect MD with an accuracy of 93.4% by using the wavelet transformation and a three-layer CNN architecture.-
dc.languageeng-
dc.relation.ispartofElectronics (Switzerland)-
dc.sourceElectronics (Switzerland), v. 8 (4), p. 1-15-
dc.subject3314 Tecnología médica-
dc.subject.otherGait Analysis-
dc.subject.otherInertial Sensors-
dc.subject.otherMachine Learning-
dc.subject.otherParkinson’S Disease-
dc.subject.otherWavelet Transformation-
dc.subject.otherWearable Sensors-
dc.titleAnalysis and classification of motor dysfunctions in arm swing in parkinson’s disease-
dc.typeinfo:eu-repo/semantics/article-
dc.typeArticle-
dc.identifier.doi10.3390/electronics8121471-
dc.identifier.scopus85076025107-
dc.identifier.isi000506678200097-
dc.contributor.authorscopusid57204115368-
dc.contributor.authorscopusid57212143523-
dc.contributor.authorscopusid56395430400-
dc.contributor.authorscopusid6602376272-
dc.identifier.eissn2079-9292-
dc.identifier.issue12-
dc.relation.volume8-
dc.investigacionCiencias de la Salud-
dc.type2Artículo-
dc.contributor.daisngid34756291-
dc.contributor.daisngid34775863-
dc.contributor.daisngid7765108-
dc.contributor.daisngid265761-
dc.description.numberofpages15-
dc.utils.revision-
dc.contributor.wosstandardWOS:Steinmetzer, T-
dc.contributor.wosstandardWOS:Maasch, M-
dc.contributor.wosstandardWOS:Bonninger, I-
dc.contributor.wosstandardWOS:Travieso, CM-
dc.date.coverdateDiciembre 2019-
dc.identifier.ulpgces
dc.description.sjr0,303
dc.description.jcr2,412
dc.description.sjrqQ2
dc.description.jcrqQ2
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptIDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
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