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dc.contributor.authorAvilés Rivero, Angélica
dc.contributor.authorAlsaleh, Samar M.
dc.contributor.authorSobrevilla Frisón, Pilar
dc.contributor.authorCasals Gelpi, Alicia
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2016-04-08T09:40:02Z
dc.date.issued2015
dc.identifier.citationAvilés, A., Alsaleh, S., Sobrevilla, P., Casals, A. Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery. A: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. "2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)". Milan: Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 1-4.
dc.identifier.isbn9781424492695
dc.identifier.urihttp://hdl.handle.net/2117/85399
dc.description.abstractThe lack of force feedback is considered one of the major limitations in Robot Assisted Minimally Invasive Surgeries. Since add-on sensors are not a practical solution for clinical environments, in this paper we present a force estimation approach that starts with the reconstruction of a 3D deformation structure of the tissue surface by minimizing an energy functional. A Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) based architecture is then presented to accurately estimate the applied forces. According to the results, our solution offers long-term stability and shows a significant percentage of accuracy improvement, ranging from about 54% to 78%, over existing approaches.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subject.lcshBiomechanics
dc.subject.lcshRobotics in medicine
dc.subject.otherSurgical robotics
dc.subject.otherVision based force estimation
dc.titleForce-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery
dc.typeConference report
dc.subject.lemacBiomecànica
dc.subject.lemacRobòtica en medicina
dc.contributor.groupUniversitat Politècnica de Catalunya. GRINS - Grup de Recerca en Robòtica Intel·ligent i Sistemes
dc.identifier.doi10.1109/EMBC.2015.7318246
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7318246
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac17567001
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorAvilés, A.; Alsaleh, S.; Sobrevilla, P.; Casals, A.
local.citation.contributorAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
local.citation.pubplaceMilan
local.citation.publicationName2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)
local.citation.startingPage1
local.citation.endingPage4


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