ArticlePeerJ2022
Antagonistic co-contraction can minimize muscular effort in systems with uncertainty.
Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antagonistic muscular co-contraction for skilled, healthy piano technique: a scoping review.Frontiers in psychology · 2025Pooled it
- Neural reservoir control of a bio-hybrid soft arm.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- How muscle ageing affects rapid goal-directed movement: mechanistic insights from a simple model.PLoS computational biology · 2026Article
- Biomechanical models in the lower-limb exoskeletons development: a review.Journal of neuroengineering and rehabilitation · 2025Review
- Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control.PLoS computational biology · 2024Article
- Muscle-Driven Predictive Physics Simulations of Quadrupedal Locomotion in the Horse.Integrative and comparative biology · 2024Article
- Learning to stand with sensorimotor delays generalizes across directions and from hand to leg effectors.Communications biology · 2024Article
- The sensorimotor system modulates muscular co-contraction relative to visuomotor feedback responses to regulate movement variability.Journal of neurophysiology · 2023Article
- Neuromuscular control: from a biomechanist's perspective.Frontiers in sports and active living · 2023Review
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
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 feedforward 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 controller 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.
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