Multi-muscle FES control of the human arm for interaction tasks - Stabilizing with muscle co-contraction and postural adjustment: A simulation study

Yu Wei Liao, Eric M. Schearer, Eric J. Perreault, Matthew C. Tresch, Kevin M. Lynch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In this paper we present a method to stimulate multiple muscles in a human arm to perform interaction tasks, using an implanted Functional Electrical Stimulation (FES) neuroprosthesis. The unstable effect arising from interaction tasks is considered, and the arm stability is directly treated as one of the control objectives in the controller design. By exploiting the kinematic and muscular redundancy of the system, we can control the interaction force and the arm's stiffness property simultaneously, thus ensuring the stable execution of interaction tasks. A representative example of such interaction tasks, namely the 'pushing with a stick' task, is simulated. It is found that using our proposed controller, as compared to a previously developed feedforward FES controller that does not consider arm stiffness or stability, the stability of the arm is guaranteed while the task of force control is correctly achieved.

Original languageEnglish (US)
Title of host publicationIROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2134-2139
Number of pages6
ISBN (Electronic)9781479969340
DOIs
StatePublished - Oct 31 2014
Event2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States
Duration: Sep 14 2014Sep 18 2014

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
Country/TerritoryUnited States
CityChicago
Period9/14/149/18/14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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