Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/31253
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dc.contributor.authorBadesa, Francisco Javier-
dc.contributor.authorGarcía Aracil, Nicolás-
dc.contributor.authorCatalán, José María-
dc.contributor.authorZollo, Loredana-
dc.contributor.authorPagliara, Silvio Marcello-
dc.contributor.authorSterzi, Silvia-
dc.contributor.authorLauretti, Clemente-
dc.contributor.authorCordella, Francesca-
dc.contributor.authorCrea, Simona-
dc.contributor.authorCiancio, Anna Lisa-
dc.contributor.authorTrigili, Emilio-
dc.contributor.authorVitiello, Nicola-
dc.contributor.otherDepartamentos de la UMH::Ingeniería de Sistemas y Automáticaes_ES
dc.date.accessioned2024-02-07T17:53:32Z-
dc.date.available2024-02-07T17:53:32Z-
dc.date.created2018-
dc.identifier.citationFrontiers in Neurorobotics Volume 12 - 2018es_ES
dc.identifier.issn1662-5218-
dc.identifier.urihttps://hdl.handle.net/11000/31253-
dc.description.abstractThe reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4- DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent14es_ES
dc.language.isoenges_ES
dc.publisherFrontiers Mediaes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectmotion planninges_ES
dc.subjectmachine learninges_ES
dc.subjectlearning by demonstrationes_ES
dc.subjectdynamics movement primitiveses_ES
dc.subjectassistive roboticses_ES
dc.titleLearning by Demonstration for Motion Planning of Upper-Limb Exoskeletonses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.3389/fnbot.2018.00005es_ES
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Artículos Ingeniería de Sistemas y Automática


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