Optimal control-driven functional electrical stimulation: A PRISMA-ScR scoping review
Bibliographic record
Abstract
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. 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. 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. 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. • 52 studies included; cohorts are mostly small and comprised young, healthy males. • No consensus on FES model selection; rationales for choices are rarely reported. • OCP mostly address joint tracking and FES activation; methods are under-reported. • Fatigue seldom modeled or measured; only six in vivo studies show reduced fatigue. • Field maturity: approximately TRL 5; no longitudinal or clinical-workflow trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".