ORCID ID
https://orcid.org/0000-0002-3791-3749
Document Type
Article
Publication Date
1-1-2022
Publication Title
PeerJ
Abstract
Muscular co-contraction of antagonistic muscle pairs is often observed in human movement, but it is considered inefficient and it can currently not be predicted in
simulations where muscular effort or metabolic energy are minimized. Here, we investigated the relationship between minimizing effort and muscular co-contraction
in systems with random uncertainty to see if muscular co-contraction can minimize effort in such system. We also investigated the effect of time delay in the muscle, by varying the time delay in the neural control as well as the activation time constant.We solved optimal control problems for a one-degree-of-freedom pendulum actuated by two identical antagonistic muscles, using forward shooting, to find controller parameters that minimized muscular effort while the pendulum remained upright in the presence of noise added to the moment at the base of the pendulum. We compared a controller with and without feed forward control. Task precision was defined by bounding the root mean square deviation from the upright position, while different perturbation levels defined task difficulty. We found that effort was minimized when the feedforward control was nonzero, even when feedforward control was not necessary to perform the task, which indicates that co-contraction can minimize effort in systems with uncertainty. We also found that the optimal level of co-contraction increased with time delay, both when the activation time constant was increased and when neural time delay was added. Furthermore, we found that for controllers with a neural time delay, a different trajectory was optimal for a ontroller with feedforward control than for one without, which indicates that simulation trajectories are dependent on the controller architecture. Future movement predictions should therefore account for uncertainty in dynamics and control, and carefully choose the controller architecture. 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.
Recommended Citation
Koelewijn, Anne D. and Van Den Bogert, Antonie J., "Antagonistic Co-Contraction Can Minimize Muscular Effort in Systems With Uncertainty" (2022). Mechanical Engineering Faculty Publications. 410.
https://engagedscholarship.csuohio.edu/enme_facpub/410
DOI
10.7717/peerj.13085
Version
Publisher's PDF
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume
10
