Validating Predictive Simulation-Derived Electrical Stimulation Cycling in a Person with Spinal Cord Injury

Functional electrical stimulation (FES) is commonly used in rehabilitation for its ability to activate muscle groups and functionally apply them. One of the most popular applications of FES is cycling for its safety and proven increase in users' health and quality of life. However, some challenges are intrinsic to the cycling application, among them, finding the best parameters of electrical stimulation to optimize the benefits for each user. While studies have focused on determining the optimal timing for stimulation with a bang-bang control strategy, there has been comparatively less research on its stimulation profile. This study develops and tests a predictive simulation-derived stimulation profile for FES cycling. We applied fully predictive simulations of FES cycling models following a straightforward framework consisting of (1) building torque-driven and muscle-driven models, (2) defining and solving an optimal control problem, (3) converting the optimal solution result to applicable FES control signal, (4) implementing the signal in an experimental setting, and lastly, (5) validating the control signal on a volunteer with spinal cord injury. The results, when compared to a bang-bang control strategy, increased in power output, cadence, and balance in all trials. As far as we know, this is the first study to successfully implement such a strategy. Our results show the potential of predictive simulations to improve the effectiveness of FES cycling rehabilitation.