Heart motion prediction based on adaptive estimation algorithms for robotic assisted beating heart surgery

Name: Heart motion prediction based on adaptive estimation algorithms for robotic assisted beating heart surgery
Author: Tuna, E. E., Franke, T. J., Bebek, Özkan, Shiose, A., Fukamachi, K., Çavuşoğlu, M. C.

İsim	Heart motion prediction based on adaptive estimation algorithms for robotic assisted beating heart surgery
Yazar	Tuna, E. E., Franke, T. J., Bebek, Özkan, Shiose, A., Fukamachi, K., Çavuşoğlu, M. C.
Basım Tarihi:	2013
Basım Yeri	- IEEE
Konu	Active relative motion canceling, Beating heart surgery, Prediction algorithm, Signal estimation, Surgical robotics
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1552-3098
Kayıt Numarası	15444835-5ed4-424c-9262-77a4306d6d01
Lokasyon	Mechanical Engineering
Tarih	2013
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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. 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.
DOI	10.1109/TRO.2012.2217676
Cilt	29

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Heart motion prediction based on adaptive estimation algorithms for robotic assisted beating heart surgery

Yazar Tuna, E. E., Franke, T. J., Bebek, Özkan, Shiose, A., Fukamachi, K., Çavuşoğlu, M. C.

Basım Tarihi 2013

Basım Yeri - IEEE

Konu Active relative motion canceling, Beating heart surgery, Prediction algorithm, Signal estimation, Surgical robotics

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1552-3098

Kayıt Numarası 15444835-5ed4-424c-9262-77a4306d6d01

Lokasyon Mechanical Engineering

Tarih 2013

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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. 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.

DOI 10.1109/TRO.2012.2217676

Cilt 29