Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic Assisted Beating Heart Surgery
Identifieur interne : 001818 ( Pmc/Curation ); précédent : 001817; suivant : 001819Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic Assisted Beating Heart Surgery
Auteurs : E. Erdem Tuna ; Timothy J. Franke ; Özkan Bebek ; Akira Shiose ; Kiyotaka Fukamachi ; M. Cenk Çavu O LuSource :
- IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society [ 1552-3098 ] ; 2012.
Abstract
Robotic 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 (ARMC). 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-square 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 3 degrees of freedom test-bed and prerecorded
Url:
DOI: 10.1109/TRO.2012.2217676
PubMed: 23976889
PubMed Central: 3747962
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<author><name sortKey="Tuna, E Erdem" sort="Tuna, E Erdem" uniqKey="Tuna E" first="E. Erdem" last="Tuna">E. Erdem Tuna</name>
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<author><name sortKey="Franke, Timothy J" sort="Franke, Timothy J" uniqKey="Franke T" first="Timothy J." last="Franke">Timothy J. Franke</name>
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<author><name sortKey="Bebek, Ozkan" sort="Bebek, Ozkan" uniqKey="Bebek O" first="Özkan" last="Bebek">Özkan Bebek</name>
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<author><name sortKey="Shiose, Akira" sort="Shiose, Akira" uniqKey="Shiose A" first="Akira" last="Shiose">Akira Shiose</name>
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<author><name sortKey="Fukamachi, Kiyotaka" sort="Fukamachi, Kiyotaka" uniqKey="Fukamachi K" first="Kiyotaka" last="Fukamachi">Kiyotaka Fukamachi</name>
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<author><name sortKey="Cavu O Lu, M Cenk" sort="Cavu O Lu, M Cenk" uniqKey="Cavu O Lu M" first="M. Cenk" last="Çavu O Lu">M. Cenk Çavu O Lu</name>
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<author><name sortKey="Franke, Timothy J" sort="Franke, Timothy J" uniqKey="Franke T" first="Timothy J." last="Franke">Timothy J. Franke</name>
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<author><name sortKey="Bebek, Ozkan" sort="Bebek, Ozkan" uniqKey="Bebek O" first="Özkan" last="Bebek">Özkan Bebek</name>
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<author><name sortKey="Shiose, Akira" sort="Shiose, Akira" uniqKey="Shiose A" first="Akira" last="Shiose">Akira Shiose</name>
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<author><name sortKey="Fukamachi, Kiyotaka" sort="Fukamachi, Kiyotaka" uniqKey="Fukamachi K" first="Kiyotaka" last="Fukamachi">Kiyotaka Fukamachi</name>
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<author><name sortKey="Cavu O Lu, M Cenk" sort="Cavu O Lu, M Cenk" uniqKey="Cavu O Lu M" first="M. Cenk" last="Çavu O Lu">M. Cenk Çavu O Lu</name>
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<front><div type="abstract" xml:lang="en"><p id="P1">Robotic 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 (ARMC). 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-square 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 3 degrees of freedom test-bed and prerecorded <italic>in</italic>
-<italic>vivo</italic>
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.</p>
</div>
</front>
</TEI>
<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101514509</journal-id>
<journal-id journal-id-type="pubmed-jr-id">36499</journal-id>
<journal-id journal-id-type="nlm-ta">IEEE Trans Robot</journal-id>
<journal-id journal-id-type="iso-abbrev">IEEE Trans Robot</journal-id>
<journal-title-group><journal-title>IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society</journal-title>
</journal-title-group>
<issn pub-type="ppub">1552-3098</issn>
<issn pub-type="epub">1941-0468</issn>
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<article-meta><article-id pub-id-type="pmid">23976889</article-id>
<article-id pub-id-type="pmc">3747962</article-id>
<article-id pub-id-type="doi">10.1109/TRO.2012.2217676</article-id>
<article-id pub-id-type="manuscript">NIHMS495910</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic Assisted Beating Heart Surgery</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Tuna</surname>
<given-names>E. Erdem</given-names>
</name>
<role>Student Member, IEEE</role>
<aff id="A1">Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA</aff>
<email>eet12@case.edu</email>
</contrib>
<contrib contrib-type="author"><name><surname>Franke</surname>
<given-names>Timothy J.</given-names>
</name>
<role>Student Member, IEEE</role>
<aff id="A2">Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA</aff>
<email>tjf6@case.edu</email>
</contrib>
<contrib contrib-type="author"><name><surname>Bebek</surname>
<given-names>Özkan</given-names>
</name>
<role>Member, IEEE</role>
<aff id="A3">Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA</aff>
<email>oxb6@case.edu</email>
</contrib>
<contrib contrib-type="author"><name><surname>Shiose</surname>
<given-names>Akira</given-names>
</name>
<aff id="A4">Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA</aff>
<email>as-1@heart.med.kyushu-u.ac.jp</email>
</contrib>
<contrib contrib-type="author"><name><surname>Fukamachi</surname>
<given-names>Kiyotaka</given-names>
</name>
<aff id="A5">Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA</aff>
<email>fukamak@ccf.org</email>
</contrib>
<contrib contrib-type="author"><name><surname>Çavuşoğlu</surname>
<given-names>M. Cenk</given-names>
</name>
<role>Senior Member, IEEE</role>
<aff id="A6">Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA</aff>
<email>cavusoglu@case.edu</email>
</contrib>
</contrib-group>
<pub-date pub-type="nihms-submitted"><day>16</day>
<month>7</month>
<year>2013</year>
</pub-date>
<pub-date pub-type="epub"><day>28</day>
<month>9</month>
<year>2012</year>
</pub-date>
<pub-date pub-type="ppub"><day>1</day>
<month>2</month>
<year>2013</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>01</day>
<month>2</month>
<year>2014</year>
</pub-date>
<volume>29</volume>
<issue>1</issue>
<fpage>261</fpage>
<lpage>276</lpage>
<abstract><p id="P1">Robotic 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 (ARMC). 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-square 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 3 degrees of freedom test-bed and prerecorded <italic>in</italic>
-<italic>vivo</italic>
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.</p>
</abstract>
<kwd-group><title>Index Terms</title>
<kwd>Surgical robotics</kwd>
<kwd>beating heart surgery</kwd>
<kwd>active relative motion canceling</kwd>
<kwd>signal estimation</kwd>
<kwd>prediction algorithm</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>
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