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Updated: May 14, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Grasp and release with surface functional electrical stimulation using a Model Predictive Control approach.

Ard J Westerveld1, Alexander Kuck, Alfred C Schouten

  • 1Institute for Biomedical Engineering and Technical Medicine (MIRA), University of Twente, 7500 AE Enschede, The Netherlands. a.j.westerveld@utwente.nl

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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Novel functional electrical stimulation (FES) approaches show promise for restoring hand function after stroke. These methods enable precise finger and thumb positioning for grasping and releasing objects, aiding in rehabilitation.

Area of Science:

  • Biomedical Engineering
  • Neurorehabilitation
  • Robotics

Background:

  • Stroke frequently results in significant hand dysfunction, impairing daily activities.
  • Effective hand function relies on precise finger and thumb positioning for grasping and releasing objects.
  • Current rehabilitation strategies for post-stroke hand recovery require innovative assistive technologies.

Purpose of the Study:

  • To evaluate the feasibility of novel functional electrical stimulation (FES) based approaches for hand grasp and release.
  • To compare the performance of a model predictive controller (MPC) with a proportional (P) feedback controller for assistive grasp and release.
  • To assess the potential application of these FES methods in stroke rehabilitation.

Main Methods:

  • Comparison of a model predictive controller (MPC) and a proportional (P) feedback controller for FES.

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  • Evaluation of controller performance in tracking reference trajectories for grasp and release.
  • Testing the ability to grasp, hold, and release objects of varying sizes using the Action Research Arm test (ARAT).

    • Main Results:

    • Both MPC and P controllers demonstrated the ability to selectively activate fingers for grasping objects of different sizes.
    • The MPC approach proved easier to implement due to its reliance on a single system model identification.
    • The P-controller required more extensive parameter tuning, increasing initialization time.

    Conclusions:

    • Novel FES-based controllers show feasibility for assisting grasp and release in post-stroke individuals.
    • The MPC controller offers a more user-friendly and efficient approach compared to the P-controller.
    • Further clinical evaluation in patients is warranted to explore the full rehabilitation potential.