2007 IEEE/RSJ International Conference on Intelligent Robots and Systems 2007
DOI: 10.1109/iros.2007.4399461
|View full text |Cite
|
Sign up to set email alerts
|

Improved prediction of heart motion using an adaptive filter for robot assisted beating heart surgery

Abstract: Robot assisted heart surgery allows surgeons to operate on a heart while it is still beating as if it had been stopped. 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 over a prediction horizon. In this paper, a prediction algorithm, using an adaptive filter … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
36
0

Year Published

2008
2008
2018
2018

Publication Types

Select...
4
3

Relationship

0
7

Authors

Journals

citations
Cited by 30 publications
(37 citation statements)
references
References 6 publications
1
36
0
Order By: Relevance
“…They reported RMS errors of 1.43 mm. Frank et al used adaptive filters to follow pre-recorded heart motion [18]. This study reported RMS errors of 0.5 mm.…”
Section: Resultsmentioning
confidence: 89%
See 2 more Smart Citations
“…They reported RMS errors of 1.43 mm. Frank et al used adaptive filters to follow pre-recorded heart motion [18]. This study reported RMS errors of 0.5 mm.…”
Section: Resultsmentioning
confidence: 89%
“…ImageRobot Predictive Control Based Dynamics [1] Prediction No No [7] Prediction No Yes [11] Prediction Yes No [18] Prediction No Yes [19] Predictive Control No Yes [20] Predictive Control No Yes Proposed Predictive Yes Yes Method Control position, and the intervening dynamics between the reference position and the actual position, which is that of a voice coil linear actuator, is neglected. Other prediction methods address heart rate variability through the use of adaptive filters, which slowly change the length of the predicted heart beat to make it coincide with the length of the actual heartbeat.…”
Section: Prediction Ormentioning
confidence: 99%
See 1 more Smart Citation
“…Frank et al proposed a new Autoregressive (AR) model based adaptive prediction technique [8,9] that is less susceptible to the variation of the heart motion period and robust to noise. However, this AR model is still a linear model that cannot describe some of the nonlinear dynamics of heart motion.…”
Section: Related Workmentioning
confidence: 99%
“…Prior research in motion compensated coronary artery bypass graft has investigated prediction using an adaptive filter bank [4], an estimator based on Takens theorem [8], and a vector autoregressive least squares estimator [9]. In this work, we employ an extended Kalman filter (EKF) with an explicity quasiperiodic model, which is effective for 3DUS-guided mitral valve motion compensation [7].…”
Section: Quasiperiodic Predictive Filter: Extended Kalman Filtermentioning
confidence: 99%