Mathematical modeling of nonisometric electrically evoked contractions of healthy human quadriceps femoris muscle
Functional Electrical Stimulation (FES) is the coordinated electrical excitation of paralyzed or weak muscles in patients with upper motor neuron lesions to produce functional movements such as walking. However, FES has had a relatively minor impact on rehabilitation. There are several underlying reasons for this shortcoming. First, the physiological and biomechanical processes involved in the generation of FES-elicited movements are non-linear and time varying. Hence, it is difficult to determine the appropriate stimulation patterns necessary to produce the desired muscle force and limb motion. Second, controlling the movements of paralyzed limbs is difficult with the commercial open loop systems. Finally, other factors such as fatigue and the influence of voluntary upper-body forces further complicate the control task. However, the use of mathematical muscle models can improve and accelerate the development of FES for practical use. Models that are accurate and predictive when used in conjunction with FES systems that monitor muscle performance, would enable stimulators to deliver patterns customized for each person to perform a particular task while continuously adapting the stimulation protocols to the actual needs of the patient. In this dissertation the development and predictive abilities of a mathematical muscle model when the muscle is held isometrically at different lengths, when the muscle is allowed to shorten and lengthen at constant velocities, and when the leg is allowed to move freely during nonisometric contractions is presented. The predictive abilities of the model were tested by comparing the model data to the experimental force and motion data. Our results showed that the model accurately predicted the force developed by muscle at different lengths and velocities during isometric and isovelocity contractions, respectively and the angular position and velocity of the lower leg during nonisometric contractions when the muscle is stimulated with a wide range of clinically relevant stimulation frequencies and patterns. Compared to other models, the current model requires very few parameters to describe the behavior of the muscle in response to electrical stimulation. This makes the current model a suitable candidate for control algorithms that can track parameter variations online and provide a better control of motion during FES.
0541: Biomedical research