Augmenting the human-machine interface: improving manual accuracy
Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, Vol. 4, pp. 3546 - 3550, April, 1997
Abstract
We present a novel application of a neural network to augment manual precision by cancelling involuntary motion. This method may be applied in microsurgery, using either a telerobotic approach or active compensation in a handheld instrument. A feedforward neural network is trained to input the measured trajectory of a handheld tool tip and output the intended trajectory. Use of the neural network decreases rms error in recordings from four subjects by an average of 43.9%.
BibTeX
@conference{Riviere-1997-14344,author = {Cameron Riviere and Pradeep Khosla},
title = {Augmenting the human-machine interface: improving manual accuracy},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {1997},
month = {April},
volume = {4},
pages = {3546 - 3550},
}
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