Optimization of a Stimulation Train based on a Predictive Model of Muscle Force and Fatigue

Optimizing stimulation frequency based on a mathematical model that can predict skeletal muscle force and fatigue may improve the effectiveness of functional electrical stimulation systems. Potentially, optimal stimulation patterns can maximize muscle force production while also delaying the onset of muscle fatigue. In this paper, dynamic optimization was used to generate an optimal, frequency varying pulse train that maintains a desired isometric knee extension without unnecessarily fatiguing the muscle. The optimization method employed a predictive mathematical model of muscle force and fatigue. Knee extension experiments were conducted on an able-bodied participant to identify the model parameters. To test the effectiveness of the optimized train to delay muscle fatigue, a second knee extension experiment was conducted to compare the performance of the optimized stimulation train and a 50Hz constant frequency train. The peak force and the force time integral of the optimized stimulation train were found to be higher than the 50Hz constant frequency train. These preliminary results show that optimizing stimulation patterns, based on a subject specific predictive mathematical model, may potentially delay the onset of muscle fatigue while obtaining desired force profiles.