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A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

dc.contributor.authorTorricelli, Diego
dc.contributor.authorCortés, Camilo
dc.contributor.authorLete, Nerea
dc.contributor.authorBertelsen, Alvaro
dc.contributor.authorGonzález-Vargas, José E.
dc.contributor.authordel-Ama, Antonio J.
dc.contributor.authorDimbwadyo, Iris
dc.contributor.authorMoreno, Juan C.
dc.contributor.authorFlorez, Julian
dc.contributor.authorPons, José L.
dc.date.accessioned2024-01-04T07:21:10Z
dc.date.available2024-01-04T07:21:10Z
dc.date.issued2018-04-27
dc.identifier.citationTorricelli D, Cortés C, Lete N, Bertelsen Á, Gonzalez-Vargas JE, Del-Ama AJ, Dimbwadyo I, Moreno JC, Florez J, Pons JL. A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait. Front Neurorobot. 2018 Apr 27;12:18es
dc.identifier.issn1662-5218
dc.identifier.urihttps://hdl.handle.net/10115/28158
dc.descriptionEsta publicación ha sido realizada en los proyectos Europeos de investigación EU FP7 BioMot (Smart Wearable Robots with Bioinspired Sensory-Motor Skills), Ref.: 611695, y el proyecto EU H2020 EUROBENCH European Robotic framework for bipedal locomotion benchmarking, Ref.: 779963. Colaboración entre instituciones de México y España. CONTRIBUCIÓN (según taxonomía CReDIT): Conpcetualization, Methodology, Project administration, Resources, Supervision, Writting: review&editing. -------------------------------------- Indicios de calidad: - A nivel del medio de difusión Revista con revisión por pares doble ciego indexada en JCR y Scupus, en el segundo cuartil (Q2) primer tercil (T1) en las categorías de Computer Science, Artificial Intelligence, Robótica y Neurociencias con factor de impacto 3.000 en el año de publicación del artículo (2018) - A nivel de aportación. El artículo ha tenido mucho impacto en las principales redes de interacción científica: Mendeley (138 accesos) y ResearchGate (2.227 accesos). Los índices de impacto son los siguientes: 25 citas en Scopus situándose en el percentil 69 de artículos similares (Scopus), 28 en WoS y 34 en GoogleScholar. Los índices de citación normalizadas son los siguientes: 0.94 (FWCI, Scoups) y 1.03 (FCR, Dimensions).es
dc.description.abstractThe relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeletones
dc.language.isoenges
dc.publisherFrontiers Media SAes
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectwearable robotes
dc.subjectbenchmarkinges
dc.subjectwalkinges
dc.subjectrehabilitationes
dc.subjectlower limbes
dc.subjectskeletal modelinges
dc.titleA Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gaites
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.3389/fnbot.2018.00018es
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses


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Atribución 4.0 InternacionalExcept where otherwise noted, this item's license is described as Atribución 4.0 Internacional