A low-cost optimization framework to solve muscle redundancy problem

Prediction of muscle activations based on optimization procedures mostly leads to a prohibitive computational effort. To overcome this problem, an optimization framework by reformulation of the so-called method of extended inverse dynamics (EID) was developed. A planar, seven-segment model with sixteen muscle groups was used to represent human neuromusculoskeletal dynamics. The muscle activations were estimated based on two methods: EID, which treats the system dynamics (compatibility between muscular and skeletal torques), as an equality constraint, and the proposed method, which employs unconstrained system dynamics of EID (USDEID). The proposed method is based on the idea that the EID equality constraint, which is difficult to satisfy, can be eliminated by reformulation of the governing equations and optimization variables, which not only relaxes the optimization problem and leads to less optimization parameters, but also guarantees the full compatibility of the system dynamics. The comparison of simulation results of optimal muscle activations against experimental data showed a reasonable agreement for both methods during half of a gait cycle. Optimization results showed that USDEID is not only more accurate than EID in terms of the compatibility between the skeletal and muscular system dynamics, but also approximately eight times faster for ten random initial values. USDEID may be used to predict muscle activations, when the computational cost becomes prohibitive.