Optimal control driven functional electrical stimulation: A scoping review

TL;DR

This scoping review maps current research on optimal control-driven FES, highlighting its potential to improve motion precision and reduce fatigue, while identifying challenges hindering clinical adoption and future research directions.

Contribution

The review synthesizes existing literature on optimal control in FES, clarifies best practices, and outlines key challenges and future research needs for clinical implementation.

Findings

Optimal control FES can improve motion accuracy and reduce fatigue.

Clinical adoption is limited by modeling inconsistencies and validation issues.

Most studies focus on simple models and healthy young participants.

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.