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General information |
| Author |
Liang, Fan; Yu, Yang; Wang, Haizhong; Meng, Xiaofeng |
| Published |
InTech Open Access Publisher, 2013
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| Abstract |
Off‐pump Coronary Artery Bypass Graft
(CABG) surgery outperforms traditional on‐pump
surgery because the assisted robotic tools can alleviate the
relative motion between the beating heart and robotic
tools. Therefore, it is possible for the surgeon to operate
on the beating heart and thus lessens post surgery
complications for the patients. Due to the highly irregular
and non‐stationary nature of heart motion, it is critical
that the beating heart motion is predicted in the modelbased
track control procedures. It is technically preferable
to model heart motion in a nonlinear way because the
characteristic analysis of 3D heart motion data through
Bi‐spectral analysis and Fourier methods demonstrates
the involved nonlinearity of heart motion. We propose an
adaptive nonlinear heart motion model based on the
Volterra Series in this paper. We also design a fast lattice
structure to achieve computational‐efficiency for realtime
online predictions. We argue that the quadratic term
of the Volterra Series can improve the prediction accuracy
by covering sharp change points and including the
motion with sufficient detail. The experiment results
indicate that the adaptive nonlinear heart motion
prediction algorithm outperforms the autoregressive
(AR) and the time‐varying Fourier‐series models in terms
of the root mean square of the prediction error and the
prediction error in extreme cases. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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