Personalized FES-cycling patterns via optimal control: from musculoskeletal simulation to experimental validation
Abstract
This study introduces an optimal control framework for functional electrical stimulation (FES) cycling that produces personalized stimulation patterns directly transferable from simulation to experimental cycling, without the need for empirical tuning. We use a physiological muscle model that accounts for the effects of electrical stimulation on muscle contraction dynamics, whose parameters are identified using experimental data obtained from a dedicated protocol conducted with a participant with spinal cord injury (SCI). We demonstrate the feasibility of obtaining a stimulation pattern via trajectory optimization to track a 3.6 rad/s (35 rpm) cadence and a 25 W crank power output by optimizing muscle stimulation pulse durations. The simulations are conducted using two different models-one comprising six muscles and the other four-to explore different experimental conditions. Finally, we assess whether the resulting stimulation patterns enable the participant to pedal a recumbent tricycle without further modification. The generated patterns successfully produce continuous, coordinated pedaling. The average power output was 18 W with a mean angular velocity of 3.93 ± 0.80 rad/s for the six-muscle configuration, and 19 W with a mean angular velocity of 3.90 ± 1.13 rad/s for the four-muscle configuration. This study introduces a physiological model-based control approach for FES cycling, bridging musculoskeletal simulation and experimental validation, and underscores the potential of predictive simulations and optimal control to personalize FES-assisted protocols.
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BibTeX
@article{coelhomagalhaes2026personalized,
title = {{Personalized FES-cycling patterns via optimal control: from musculoskeletal simulation to experimental validation}},
author = {Tiago Coelho-Magalhães and Christine Azevedo-Coste and Henrique Resende-Martins and François Bailly},
journal = {Journal of NeuroEngineering and Rehabilitation},
year = {2026},
month = sep,
doi = {10.1186/s12984-026-02136-x},
url = {https://inria.hal.science/hal-05738944v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Personalized FES-cycling patterns via optimal control: from musculoskeletal simulation to experimental validation
AU - Tiago Coelho-Magalhães
AU - Christine Azevedo-Coste
AU - Henrique Resende-Martins
AU - François Bailly
PY - 2026
DA - 2026/09/04
JO - Journal of NeuroEngineering and Rehabilitation
DO - 10.1186/s12984-026-02136-x
UR - https://inria.hal.science/hal-05738944v1
AB - This study introduces an optimal control framework for functional electrical stimulation (FES) cycling that produces personalized stimulation patterns directly transferable from simulation to experimental cycling, without the need for empirical tuning. We use a physiological muscle model that accounts for the effects of electrical stimulation on muscle contraction dynamics, whose parameters are identified using experimental data obtained from a dedicated protocol conducted with a participant with spinal cord injury (SCI). We demonstrate the feasibility of obtaining a stimulation pattern via trajectory optimization to track a 3.6 rad/s (35 rpm) cadence and a 25 W crank power output by optimizing muscle stimulation pulse durations. The simulations are conducted using two different models-one comprising six muscles and the other four-to explore different experimental conditions. Finally, we assess whether the resulting stimulation patterns enable the participant to pedal a recumbent tricycle without further modification. The generated patterns successfully produce continuous, coordinated pedaling. The average power output was 18 W with a mean angular velocity of 3.93 ± 0.80 rad/s for the six-muscle configuration, and 19 W with a mean angular velocity of 3.90 ± 1.13 rad/s for the four-muscle configuration. This study introduces a physiological model-based control approach for FES cycling, bridging musculoskeletal simulation and experimental validation, and underscores the potential of predictive simulations and optimal control to personalize FES-assisted protocols.
SN - 1743-0003
ER -