The objective of this Thesis is to show that a neuromusclar model can be used to calculate an optimized train that will impede the onset of muscle fatigue while tracking a pre-defined force reference. Implementation of a predictive force and fatigue model of a human skeletal muscle when stimulated with neuromuscular electrical stimulation (NMES) is presented herein. In this implementation, the nonlinear model is used to control muscle force to a reference. Muscle control is optimized in such a way that muscle fatigue is minimized, demonstrating potential for improvements in applications where NMES use is limited by muscle fatigue. Model parameters were identified for the able-bodied subjects and testing was performed to identify the response of the muscle to a constant frequency stimulation. An optimization algorithm was then used to compute a pulse train that will maintain an isometric contraction at a constant force for a period of time without unnecessarily fatiguing the muscle. Following each train, the fatigue of the muscle was evaluated to determine if muscle fatigue was reduced. The study has concluded that muscle fatigue was significantly reduced when an optimized train is used when compared to a constant frequency train.
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