Applying Optimal Control Algorithm to Estimate Muscle Activation during the Gait Cycle

Abstract

The effective procedure for activating muscles and improving movements in individuals who have a central nervous system injury is Functional electrical stimulation (FES). To have accurate movements of the limbs, appropriate stimulation patterns should be generated and exerted on the muscles. The challenging problem is the redundancy in the musculoskeletal system that makes it necessary to use optimal control to derive muscle activations. For doing this, the redundant musculoskeletal model of the ankle joint is derived in this paper, then an optimal control method for estimating muscle activation during the gait cycle is designed. The aim of optimal control theory is to minimize the cost function, including muscle activation as the control input and joint angle tracking error as the output of the model. Two optimal control algorithms are implemented in this research. These approaches present direct and indirect control methods to solve the optimization problem of ankle movement achieved by experimental research on two healthy males during the gait cycle. Based on the results, the direct method is smoother for nonlinear systems and estimates desired muscle activation more precisely and the cost function coefficients are chosen with logical and less values in comparison to the indirect method.

Keywords

• functional electrical stimulation
• optimal control algorithm
• human muscle activation
• musculoskeletal model
• gait cycle