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Published on: January 10, 2013
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.
Introduction:
Rehabilitation after a neurological impairment can be supported by functional electrical stimulation (FES). However, FES is limited by early muscle fatigue, slowing down the recovery progress. The use of optimal control to reduce overstimulation and improve motion precision is gaining interest. This scoping review maps the current literature on optimal control for FES, clarifies best practices, persistent challenges, and outlines future research directions.
Methods:
Following the PRISMA-ScR guidelines, a search was conducted up to September 2025 using the combined keywords "FES", "optimal control" or "fatigue" across five databases (Medline, Embase, CINAHL Complete, Web of Science, and ProQuest Dissertations & Theses Citation Index). Inclusion criteria included the use of optimal control with FES for healthy individuals and those with neuromuscular disorders.
Results:
Among the 52 included studies, 25 were in silico and 27 in vivo, involving 94 participants, predominantly healthy young men. Twelve different motor tasks were investigated, with a focus on single-joint lower-limb movements. These studies principally used simple FES models, modulating pulse width or intensity to track joint angle.
Conclusions:
Optimal control-driven FES can produce accurate motions and reduce fatigue. Yet clinical adoption is slowed down by the lack of consensus on modeling approaches, inconvenient model identification protocols and limited validation. Additional barriers include insufficient open-science practices, inconsistent computational performance reporting and limited customizable commercial hardware availability. Comparative FES model studies and longitudinal trials with large cohorts, among other efforts, are required to improve the technology readiness level. Such advances would help clinical adoption and improve patient outcomes.
