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Related Experiment Video

Updated: Jan 11, 2026

Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function
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Optimal control-driven functional electrical stimulation: A PRISMA-ScR scoping review.

Kevin Co1, Mickaël Begon2, François Bailly3

  • 1Laboratoire de Simulation et Modélisation du Mouvement, Université de Montréal, Montreal, Qc, Canada.

Computers in Biology and Medicine
|November 19, 2025
PubMed
Summary
This summary is machine-generated.

Optimal control enhances functional electrical stimulation (FES) by reducing muscle fatigue and improving motion precision. Further research is needed to address challenges hindering widespread clinical adoption of this technology.

Keywords:
Biomechanical modelingClosed-loop systemsModel predictive controlMuscle fatigueNeuromuscular recoveryPersonalized rehabilitationStimulation optimization

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Control Systems

Background:

  • Functional electrical stimulation (FES) aids neurological rehabilitation but suffers from muscle fatigue, limiting recovery.
  • Optimal control strategies are increasingly explored to mitigate FES-induced overstimulation and enhance movement accuracy.
  • This review synthesizes current knowledge on optimal control applications in FES.

Purpose of the Study:

  • To map the existing literature on optimal control for FES.
  • To identify best practices and persistent challenges in the field.
  • To outline future research directions for optimizing FES technology.

Main Methods:

  • A comprehensive scoping review following PRISMA-ScR guidelines.
  • Searched five major databases up to September 2025 using keywords: "FES", "optimal control", "fatigue".
  • Included studies utilized optimal control with FES in healthy individuals and those with neuromuscular disorders.

Main Results:

  • 52 studies were included (25 in silico, 27 in vivo) with 94 participants, mostly healthy young men.
  • Research focused on single-joint lower-limb movements, employing simple FES models.
  • Studies modulated pulse width or intensity to achieve precise joint angle tracking.

Conclusions:

  • Optimal control-FES effectively generates accurate movements and reduces fatigue.
  • Clinical implementation faces barriers: lack of consensus on modeling, difficult model identification, and limited validation.
  • Advancing technology readiness requires comparative FES model studies, longitudinal trials, and improved open-science practices for better patient outcomes.