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  5. Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)
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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/13687
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dc.contributor.authorRigas, G.en
dc.contributor.authorTzallas, A. T.en
dc.contributor.authorTsipouras, M. G.en
dc.contributor.authorBougia, P.en
dc.contributor.authorTripoliti, E. E.en
dc.contributor.authorBaga, D.en
dc.contributor.authorFotiadis, D. I.en
dc.contributor.authorTsouli, S. G.en
dc.contributor.authorKonitsiotis, S.en
dc.date.accessioned2015-11-24T17:32:12Z-
dc.date.available2015-11-24T17:32:12Z-
dc.identifier.issn1089-7771-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/13687-
dc.rightsDefault Licence-
dc.subjecthidden markov models (hmms)en
dc.subjectlevodopa-induced dyskinesia (lid)en
dc.subjectparkinson's disease (pd)en
dc.subjectposture recognitionen
dc.subjecttremoren
dc.subjectquantificationen
dc.subjectmovementen
dc.subjectaccelerometryen
dc.subjectrecognitionen
dc.subjectvalidationen
dc.subjectpostureen
dc.subjectmotionen
dc.subjectsystemen
dc.titleAssessment of Tremor Activity in the Parkinson's Disease Using a Set of Wearable Sensorsen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDoi 10.1109/Titb.2011.2182616-
heal.identifier.secondary<Go to ISI>://000303997700021-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικώνel
heal.publicationDate2012-
heal.abstractTremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87% accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.en
heal.journalNameIeee Transactions on Information Technology in Biomedicineen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
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