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Abstract

Hand grasp is a complex system that plays an important role in the activities of daily living. Upper-limb neuroprostheses aim at restoring lost reaching and grasping functions on people suffering from neural disorders. However, the dimensionality and complexity of the upper-limb makes the neuroprostheses modeling and control challenging. In this work we present preliminary results for checking the feasibility of using a reinforcement learning (RL) approach for achieving grasp functions with a surface multi-field neuroprosthesis for grasping. Grasps from 20 healthy subjects were recorded to build a reference for the RL system and then two different award strategies were tested on simulations based on neuro-fuzzy models of hemiplegic patients. These first results suggest that RL might be a possible solution for obtaining grasp function by means of multi-field neuroprostheses in the near future.

Keywords

Neuroprostheses Functional electrical stimulation Grasp Reinforcement learning Modeling and control 

Notes

Acknowledgements

Authors would like to thank to Intelligent Control Research Group of UPV/EHU for giving the means of carrying out this work and to the Health Division of Tecnalia for its continuous support.

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Authors and Affiliations

  • Eukene Imatz-Ojanguren
    • 1
    • 2
    Email author
  • Eloy Irigoyen
    • 1
  • Thierry Keller
    • 2
  1. 1.Intelligent Control Research GroupUPV/EHU - University of the Basque CountryBilbaoSpain
  2. 2.TECNALIA Research and InnovationDonostia-San SebastiánSpain

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