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Modulation of Grasping Force in Prosthetic Hands Using Neural Network-Based Predictive Control

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Artificial Neural Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1260))

Abstract

This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers’ dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.

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Acknowledgement

This work was supported by the NIH NCRR INBRE grant P20RR016456, and the Louisiana Board of Regents RCS LEQSF(2007-10)-RD-A-20.

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Authors and Affiliations

  1. Electronics Core—Medical Device Solutions, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Cristian F. Pasluosta

  2. Biomedical Engineering, Rose-Hulman Institute of Technology, 5500 Wabash Ave, Terre Haute, IN, 47803, USA

    Alan W. L. Chiu

Authors
  1. Cristian F. Pasluosta
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  2. Alan W. L. Chiu
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Corresponding author

Correspondence to Alan W. L. Chiu .

Editor information

Editors and Affiliations

  1. Chemistry Department, Oxford University, UK, and University of Victoria, Victoria, British Columbia, Canada

    Hugh Cartwright

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Pasluosta, C.F., Chiu, A.W.L. (2015). Modulation of Grasping Force in Prosthetic Hands Using Neural Network-Based Predictive Control. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_11

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  • DOI: https://doi.org/10.1007/978-1-4939-2239-0_11

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-2238-3

  • Online ISBN: 978-1-4939-2239-0

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