Neural Network Methods for Error Canceling in Human-Machine Manipulation

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 complimentary 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.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA411527

Entities

People

  • Cameron N. Riviere
  • Wei T. Ang

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Computing System Architectures
  • Data Sets
  • Errors
  • Filtration
  • Frequency
  • Hall Effect
  • Hall Effect Sensors
  • Information Science
  • Kalman Filtering
  • Kalman Filters
  • Linear Systems
  • Microsurgery
  • Movement Disorders
  • Network Architecture
  • Neural Networks
  • Noise

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks