Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/120400
Campo DC Valoridioma
dc.contributor.authorBensalah, Asmaen_US
dc.contributor.authorFornés, Aliciaen_US
dc.contributor.authorCarmona-Duarte, Cristinaen_US
dc.contributor.authorLladós, Josepen_US
dc.date.accessioned2023-02-03T20:08:41Z-
dc.date.available2023-02-03T20:08:41Z-
dc.date.issued2022en_US
dc.identifier.isbn978-3-031-19744-4en_US
dc.identifier.issn0302-9743en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/120400-
dc.description.abstractAssessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case.en_US
dc.languageengen_US
dc.publisherSpringeren_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [ISSN 0302-9743], v. 13424 LNCS, p. 336-348, (Enero 2022)en_US
dc.subject1203 Ciencia de los ordenadoresen_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherDeep Learningen_US
dc.subject.otherJerken_US
dc.subject.otherMovement Classificationen_US
dc.subject.otherMovement Smoothnessen_US
dc.subject.otherNeurorehabilitationen_US
dc.subject.otherUpper-Limben_US
dc.titleEasing automatic neurorehabilitation via classification and smoothness analysisen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.relation.conferenceIGS2021: The 20th Conference of the International Graphonomics Societyen_US
dc.identifier.doi10.1007/978-3-031-19745-1_25en_US
dc.identifier.scopus85144815069-
dc.contributor.orcid0000-0002-2405-9811-
dc.contributor.orcid0000-0002-9692-5336-
dc.contributor.orcid0000-0002-4441-6652-
dc.contributor.orcid0000-0002-4533-4739-
dc.contributor.authorscopusid57222983780-
dc.contributor.authorscopusid23396320300-
dc.contributor.authorscopusid58030744300-
dc.contributor.authorscopusid6603062543-
dc.identifier.eissn1611-3349-
dc.description.lastpage348en_US
dc.description.firstpage336en_US
dc.relation.volume13424 LNCSen_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.description.notasIncluding Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformaticsen_US
dc.identifier.eisbn978-3-031-19745-1-
dc.utils.revisionen_US
dc.date.coverdateEnero 2022en_US
dc.identifier.conferenceidevents149971-
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr0,32
dc.description.sjrqQ3
dc.description.miaricds10,0
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-4441-6652-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameCarmona Duarte, María Cristina-
Colección:Actas de congresos
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