Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic Assisted Beating Heart Surgery.
Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic Assisted Beating Heart Surgery.
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DOI：
10.1109/tro.2012.2217676
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发表时间：
2013-02-01
期刊：
影响因子：
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通讯作者：
Cavuşoğlu MC
中科院分区：
文献类型：
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作者：
Tuna EE;Franke TJ;Bebek O;Shiose A;Fukamachi K;Cavuşoğlu MC
机器人辅助心脏跳动手术旨在允许外科医生在没有稳定器的情况下对跳动的心脏进行手术，就像心脏静止一样。该机器人通过密切跟踪心脏表面上的兴趣点（POI）来主动消除心脏运动，这一过程称为主动相对运动消除（ARMC）。由于POI运动的高带宽，有必要为控制器提供预测时域上的POI运动的近期未来的估计，以便实现足够的跟踪精度。在本文中，两个最小二乘预测算法，使用自适应滤波器来产生未来的位置估计，实现和研究。第一种方法假设预测时域中连续样本之间的线性系统关系。相反，第二种方法独立地为整个水平线上的每个点执行此参数化。预测参数和心率的变化对跟踪性能的影响进行了研究与恒定和变化的心率数据。使用3自由度试验台和预先记录的体内运动数据评估预测因子。然后，一步预测和跟踪性能的方法进行了比较，与扩展卡尔曼滤波预测器。最后，总结了所提出的预测算法的基本特征。
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 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.