Accurate predictions of joint contact forces through computer simulation of musculoskeletal dynamics can provide insight, in a non-invasive manner, into the joint loads of patients with osteoarthritis and healthy controls. The current approach to assume optimal control, in terms of metabolic energy expenditure, remains a major limitation of the prediction of muscle activation patterns that determine joint contact forces. Stochastically optimal muscle control, in the form of a stochastic component superimposed to the optimal control, could potentially explain the inter-trial variability as observed in measured knee contact forces during level walking. A probabilistic approach was used to predict sets of possible muscle activation patterns within a 5 and 10% limit from the optimal muscle activation pattern. The knee contact forces determined by both the optimal and stochastically optimal muscle activation patterns were compared to the corresponding knee contact force patterns measured by an instrumented implant. The range of muscle control patterns captured the inter-trial variability of knee contact forces for most of the gait cycle, suggesting that the probabilistic approach used here is representative of a stochastically optimal control that accounts for co-contraction, whereas during some time intervals a more explicit representation of the motor control strategy is required. These findings underline the importance of stochastically optimal muscle control in the prediction of knee forces within a multi-body dynamics approach.

中文翻译：

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不受控制的流形理论可以解释在水平行走过程中膝盖接触力的试验间变异性的一部分。

通过计算机模拟肌肉骨骼动力学来准确预测关节接触力，可以以非侵入性方式洞察骨关节炎患者和健康对照者的关节负荷。就新陈代谢能量消耗而言，当前采取最佳控制的方法仍然是确定关节接触力的肌肉激活模式预测的主要限制。随机最优肌肉控制以随机成分的形式叠加到最优控制上，可以潜在地解释在水平步行过程中在测得的膝盖接触力中观察到的试验间变异性。概率方法用于根据最佳肌肉激活模式预测在5％和10％范围内的可能的肌肉激活模式集。将由最佳和随机最佳肌肉激活模式确定的膝盖接触力与通过仪器植入物测量的相应膝盖接触力模式进行比较。肌肉控制模式的范围捕获了大部分步态周期中膝盖接触力的试验间变化，这表明此处使用的概率方法代表了考虑共收缩的随机最优控制，而在某些时间间隔内，需要更明确地表示电动机控制策略。这些发现强调了在多体动力学方法中预测膝盖力量的随机最佳肌肉控制的重要性。