Optimal control driven functional electrical stimulation: A scoping review
Bibliographic record
Abstract
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 review aims to map the current literature state meanwhile clarifying the best practices, identifying persistent challenges, and outlining directions for future research. Methods: Following the PRISMA guidelines, a search was conducted up to February 2024 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 44 included studies, half were in silico and half in vivo, involving 87 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 deliver precise motions and reduce fatigue. Yet clinical adoption is slowed down by the lack of consensus about modelling, inconvenient model identification protocol and limited validation. Additional barriers include insufficient open-science practices, computational performance reporting and the availability of customizable commercial hardware. 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".