Department of Movement Sciences, KU Leuven, Leuven, Belgium.
W.H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States of America.
PLoS Comput Biol. 2022 Jun 8;18(6):e1009338. doi: 10.1371/journal.pcbi.1009338. eCollection 2022 Jun.
Optimal control simulations have shown that both musculoskeletal dynamics and physiological noise are important determinants of movement. However, due to the limited efficiency of available computational tools, deterministic simulations of movement focus on accurately modelling the musculoskeletal system while neglecting physiological noise, and stochastic simulations account for noise while simplifying the dynamics. We took advantage of recent approaches where stochastic optimal control problems are approximated using deterministic optimal control problems, which can be solved efficiently using direct collocation. We were thus able to extend predictions of stochastic optimal control as a theory of motor coordination to include muscle coordination and movement patterns emerging from non-linear musculoskeletal dynamics. In stochastic optimal control simulations of human standing balance, we demonstrated that the inclusion of muscle dynamics can predict muscle co-contraction as minimal effort strategy that complements sensorimotor feedback control in the presence of sensory noise. In simulations of reaching, we demonstrated that nonlinear multi-segment musculoskeletal dynamics enables complex perturbed and unperturbed reach trajectories under a variety of task conditions to be predicted. In both behaviors, we demonstrated how interactions between task constraint, sensory noise, and the intrinsic properties of muscle influence optimal muscle coordination patterns, including muscle co-contraction, and the resulting movement trajectories. Our approach enables a true minimum effort solution to be identified as task constraints, such as movement accuracy, can be explicitly imposed, rather than being approximated using penalty terms in the cost function. Our approximate stochastic optimal control framework predicts complex features, not captured by previous simulation approaches, providing a generalizable and valuable tool to study how musculoskeletal dynamics and physiological noise may alter neural control of movement in both healthy and pathological movements.
最优控制模拟表明，肌肉骨骼动力学和生理噪声都是运动的重要决定因素。然而，由于可用计算工具效率有限，运动的确定性模拟侧重于准确建模肌肉骨骼系统，而忽略了生理噪声，而随机模拟则考虑了噪声，同时简化了动力学。我们利用了最近的方法，即使用确定性最优控制问题来近似随机最优控制问题，这可以使用直接配置法有效地解决。因此，我们能够将随机最优控制的预测扩展为一种运动协调理论，包括肌肉协调和非线性肌肉骨骼动力学产生的运动模式。在人类站立平衡的随机最优控制模拟中，我们证明了肌肉动力学的纳入可以预测肌肉协同收缩，作为一种最小努力策略，在存在感官噪声的情况下补充感觉运动反馈控制。在到达的模拟中，我们证明了非线性多节肌肉骨骼动力学能够预测在各种任务条件下的复杂受扰和未受扰的到达轨迹。在这两种行为中，我们展示了任务约束、感官噪声和肌肉固有特性之间的相互作用如何影响最佳肌肉协调模式，包括肌肉协同收缩和由此产生的运动轨迹。我们的方法能够确定真正的最小努力解决方案，因为可以明确施加任务约束，例如运动准确性，而不是在成本函数中使用惩罚项来近似。我们的近似随机最优控制框架预测了复杂的特征，这些特征无法被以前的模拟方法捕捉到，为研究肌肉骨骼动力学和生理噪声如何改变健康和病理运动中的神经控制提供了一种通用且有价值的工具。
