Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation

  • Chris T. Freeman
  • Eric Rogers
  • Jane H. Burridge
  • Ann-Marie Hughes
  • Katie L. Meadmore

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Also part of the SpringerBriefs in Control, Automation and Robotics book sub series (BRIEFSCONTROL)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore
    Pages 1-2
  3. Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore
    Pages 3-16
  4. Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore
    Pages 17-24
  5. Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore
    Pages 25-61
  6. Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore
    Pages 63-91
  7. Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore
    Pages 93-116
  8. Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore
    Pages 117-120
  9. Back Matter
    Pages 121-124

About this book

Introduction

Iterative learning control (ILC) has its origins in the control of processes that perform a task repetitively with a view to improving accuracy from trial to trial by using information from previous executions of the task. This brief shows how a classic application of this technique – trajectory following in robots – can be extended to neurological rehabilitation after stroke.
Regaining upper limb movement is an important step in a return to independence after stroke, but the prognosis for such recovery has remained poor. Rehabilitation robotics provides the opportunity for repetitive task-oriented movement practice reflecting the importance of such intense practice demonstrated by conventional therapeutic research and motor learning theory. Until now this technique has not allowed feedback from one practice repetition to influence the next, also implicated as an important factor in therapy. The authors demonstrate how ILC can be used to adjust external functional electrical stimulation of patients’ muscles while they are repeatedly performing a task in response to the known effects of stimulation in previous repetitions. As the motor nerves and muscles of the arm reaquire the ability to convert an intention to move into a motion of accurate trajectory, force and rapidity, initially intense external stimulation can now be scaled back progressively until the fullest possible independence of movement is achieved.

Keywords

Clinical Evaluation Healthcare Robotics Learning Control Medical Engineering Stroke Rehabilitation

Authors and affiliations

  • Chris T. Freeman
    • 1
  • Eric Rogers
    • 2
  • Jane H. Burridge
    • 3
  • Ann-Marie Hughes
    • 4
  • Katie L. Meadmore
    • 5
  1. 1.Department of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUnited Kingdom
  2. 2.Department of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUnited Kingdom
  3. 3.Faculty of Health SciencesUniversity of SouthamptonSouthamptonUnited Kingdom
  4. 4.Faculty of Health SciencesUniversity of SouthamptonSouthamptonUnited Kingdom
  5. 5.School of PsychologyUniversity of SouthamptonSouthamptonUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-6726-6
  • Copyright Information The Author(s) 2015
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-4471-6725-9
  • Online ISBN 978-1-4471-6726-6
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • About this book
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