AUTHOR=Warner Holly , Audu Musa L. , Labrozzi Gabrielle C. , Makowski Nathaniel S. , Triolo Ronald J. 

TITLE=Experimental feasibility of personalized functional neuromuscular stimulation stepping patterns developed in silico

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1609734

DOI=10.3389/fbioe.2025.1609734

ISSN=2296-4185

ABSTRACT=Functional neuromuscular stimulation is a technique for restoring mobility impaired by spinal cord injury, including stepping. Typically, functional neuromuscular stimulation patterns are determined by manually tuning stimulation timing and charge applied to peripheral nerves by modulating constant current pulse amplitude, width, or frequency. Manual tuning is time consuming and suboptimal; we propose an in silico alternative relying on optimal control for developing temporal patterns of stimulation that can be implemented in real-life functional neuromuscular stimulation systems. The functional neuromuscular stimulation system user model includes only those muscles available for activation with an existing functional neuromuscular stimulation system; optimal control goals and constraints emphasize simplicity to allow solutions to differ from neurotypical neuromuscular behavior. Reduction of stimulation levels and upper extremity effort during stepping are prioritized in the optimal control problem. A single study participant with incomplete spinal cord injury walked with both model-optimized and manually tuned functional neuromuscular stimulation patterns to determine the relative benefits of each. The optimized pattern reduced charge delivery by an average of 58% (35%–80% for eight of nine muscles) and improved the comfortability of left side muscle contractions. Relative to the manually tuned pattern, the model-optimized stimulation decreased upper extremity effort by 10.5% during left swing. Participant-informed modeling combined with optimal control could lead to efficient, personalized stimulation patterns.