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Augmenting the human-machine interface: improving manual accuracy

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2 Author(s)
Riviere, C.N. ; Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Khosla, P.K.

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%

Published in:

Robotics and Automation, 1997. Proceedings., 1997 IEEE International Conference on  (Volume:4 )

Date of Conference:

20-25 Apr 1997

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