This paper investigates the effect of intrinsic muscle stiffness on neural control parameters in biological musculoskeletal control of stabilisation or reaching tasks. Current model implementations of intrinsic muscle properties are highly simplified, limiting their accuracy in replicating experimental short-range stiffness (SRS) behaviour, which appears to be important for stabilisation tasks. The Hill model, often used in musculoskeletal simulations, cannot account for SRS, while the Huxley model, which can account for non-linear muscle phenomena such as SRS , has a higher computational burden. The study compares a simplified Huxley-type model to two Hill-type models and determines the effect of intrinsic SRS on the control parameters of stabilizing 1- and 2-Degree of Freedom musculoskeletal models over various positive and negative stiffness positions in the force-length curve. Furthermore, the effect of the intrinsic muscle stiffness on the robustness of the feedback parameters of simple individual muscle feedback systems is determined in reaching experiments similar to classic experiments.

The study finds that the Huxley model shows positive SRS in the negative flank of the force-length curve, achieves stabilisation through only co-contraction using a lower level of required muscle excitation than both Hill-type models and stabilises both musculoskeletal systems at a larger muscle range than the Hill-type models, including in the negative stiffness flank. The feedback parameters dominantly responsible for muscle activation patterns are also more robust to change in the Huxley model. These findings suggest that intrinsic muscle stiffness impacts neural control parameters in stabilisation and reaching tasks, and further musculoskeletal modelling should consider using more complex muscle stiffness calculations for improved accuracy.