Abstract
Muscular co-contraction is often seen in human movement, but can currently not be predicted in simulations where muscle activation or metabolic energy is minimised. Here, we intend to show that minimal-effort optimisations can predict co-contraction in systems with random uncertainty. Secondly, we aim to show that this is because of mechanical muscle properties and time delay. We used a model of a one-degree-of-freedom arm, actuated by two identical antagonistic muscles, and solved optimal control problems to find the controller that minimised muscular effort while remaining upright in the presence of noise with different levels. Tasks were defined by bound-ing the maximum deviation from the upright position, representing different levels of difficulty. We found that a controller with co-contraction required less effort than purely reactive control. Furthermore, co-contraction was optimal even without ac-tivation dynamics, since nonzero activation still allowed for faster force generation. Co-contraction is especially optimal for difficult tasks, represented by a small maxi-mum deviation, or in systems with high uncertainty. The ability of models to predict co-contraction from effort or energy minimization has important clinical and sports applications. If co-contraction is undesirable, one should aim to remove the cause of co-contraction rather than the co-contraction itself.
Competing Interest Statement
The authors have declared no competing interest.
