Electrical stimulation can be used to assist paralyzed individuals to perform functional movements. This application is called functional electrical stimulation (FES). However, muscle fatigue is a major limitation in the practical use of FES. Mathematical models that can accurately predict muscle forces and fatigue produced in response to electrical stimulations will allow us to design stimulation patterns that maximize force and minimize fatigue. The goal of this dissertation is to develop and test mathematical models that can predict changes in isometric forces during repetitive activation of human skeletal muscle with brief bursts of stimulation pulses. Results in Chapter 2 showed that our force model, parameterized with force responses to only two brief stimulation trains, was able to predict forces to trains with a wide range of stimulation frequencies and pulse patterns (the distribution of pulses within the stimulation train). In addition, the force model successfully identified the patterns that produced the greatest force-time integral (area under the force-time curve) for each individual (N = 12) under non-fatigue and fatigue conditions. The approach used to model fatigue was to monitor the changes in the force-model parameter values during fatigue. The fatigue model, parameterized with one fatigue protocol, accurately predicted the effects of stimulation frequency, pulse pattern, and resting time (time separating stimulation trains) on muscle fatigue. The success with the force- and fatigue-model systems demonstrated their potential for identifying the optimal pattern that maximize force and minimize fatigue during clinical application of FES.
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