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dc.contributor.authorTuna, E. E.
dc.contributor.authorFranke, T. J.
dc.contributor.authorBebek, Özkan
dc.contributor.authorShiose, A.
dc.contributor.authorFukamachi, K.
dc.contributor.authorÇavuşoğlu, M. C.
dc.date.accessioned2016-02-17T11:05:44Z
dc.date.available2016-02-17T11:05:44Z
dc.date.issued2013
dc.identifier.issn1552-3098
dc.identifier.urihttp://hdl.handle.net/10679/2858
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6316186
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractRobotic-assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface - a process called active relative motion canceling. Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-squares-based prediction algorithms, using an adaptive filter to generate future position estimates, are implemented and studied. The first method assumes a linear system relation between the consecutive samples in the prediction horizon. On the contrary, the second method performs this parametrization independently for each point over the whole the horizon. The effects of predictor parameters and variations in heart rate on tracking performance are studied with constant and varying heart rate data. The predictors are evaluated using a three-degree-of-freedom (DOF) test bed and prerecorded in vivo motion data. Then, the one-step prediction and tracking performances of the presented approaches are compared with an extended Kalman filter predictor. Finally, the essential features of the proposed prediction algorithms are summarized.
dc.description.sponsorshipNSF ; National Institutes of Health ; Case Western Reserve University
dc.language.isoengen_US
dc.publisherIEEE
dc.relation.ispartofIEEE Transactions on Robotics
dc.rightsrestrictedAccess
dc.titleHeart motion prediction based on adaptive estimation algorithms for robotic assisted beating heart surgeryen_US
dc.typeArticleen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID43734
dc.contributor.ozuauthorBebek, Özkan
dc.identifier.volume29
dc.identifier.issue1
dc.identifier.startpage261
dc.identifier.endpage276
dc.identifier.wosWOS:000314837100020
dc.identifier.doi10.1109/TRO.2012.2217676
dc.subject.keywordsActive relative motion canceling
dc.subject.keywordsBeating heart surgery
dc.subject.keywordsPrediction algorithm
dc.subject.keywordsSignal estimation
dc.subject.keywordsSurgical robotics
dc.identifier.scopusSCOPUS:2-s2.0-84873343274


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