An efficient simulation method for discrete-value controlled large-scale neuromyoskeletal system models

An efficient Euler-Adams hybrid integration scheme for simulating on the computer discrete-value controlled large-scale neuromyoskeletal system models is presented. If, as discussed in the model, the differential equations describing the recruitment and excitation dynamics of the muscular subsystem are independent of the corresponding contraction-dynamical state variables, they can be integrated separately over certain time intervals by a modified Euler routine that handles discontinuous right-hand sides efficiently. The resulting myostates can then be stored and used as continuous input values for the subsequent integration by an Adams predictor-corrector algorithm of the remaining contraction-dynamical and skeletomechanical state differential equations. With such an Euler-Adams hybrid integration routine one avoids the detrimental effects and efficiency losses associated with frequent stop-restart cycles of otherwise efficient Adams-type algorithms, which cycles are forced by discontinuities on the right-hand side of the myostate equations. In the example presented, a reduction in the execution time by a factor of about 5 could be achieved by implementing the proposed technique.