Neural network methods for error canceling in human-machine manipulation

Wei-Tech Ang and Cameron Riviere
Proc. 23rd Annual Intl. Conf. IEEE Engineering in Medicine and Biology Society, October, 2001, pp. 3462-3465.


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Abstract
A neural network technique is employed to cancel hand motion error during microsurgery. A cascade-correlation neural network trained via extended Kalman filtering was tested on 15 recordings of hand movement collected from 4 surgeons. The neural network was trained to output the surgeon's desired motion, suppressing erroneous components. In experiments this technique reduced the root mean square error (rmse) of the erroneous motion by an average of 39.5%. This was 9.6% greater than the reduction achieved in earlier work, which followed the complementary approach of estimating the error rather than the desired component. Preliminary results are also presented from tests in which training and testing data were taken from different surgeons.

Keywords
microsurgery, accuracy, tremor, robotics

Notes
Sponsor: The Pittsburgh Foundation
Grant ID: R. Green Annan Medical Fund and Lettie B. Trognitz Fund
Associated Center(s) / Consortia: Medical Robotics Technology Center
Associated Lab(s) / Group(s): Surgical Mechatronics Laboratory
Associated Project(s): Micron: Intelligent Microsurgical Instruments
Note: also funded by NIH (grant no. R21 RR13383) and NSF (grant no. EEC-9731748)

Text Reference
Wei-Tech Ang and Cameron Riviere, "Neural network methods for error canceling in human-machine manipulation," Proc. 23rd Annual Intl. Conf. IEEE Engineering in Medicine and Biology Society, October, 2001, pp. 3462-3465.

BibTeX Reference
@inproceedings{Ang_2001_4092,
   author = "Wei-Tech Ang and Cameron Riviere",
   title = "Neural network methods for error canceling in human-machine manipulation",
   booktitle = "Proc. 23rd Annual Intl. Conf. IEEE Engineering in Medicine and Biology Society",
   pages = "3462-3465",
   month = "October",
   year = "2001",
   Notes = "also funded by NIH (grant no. R21 RR13383) and NSF (grant no. EEC-9731748)"
}