Neuromusculoskeletal simulation provides a promising platform to inform the design of assistive devices or inform rehabilitation. For these applications, a simulation must be able to accurately represent the person of interest, such as an individual with a neurologic injury. If a simulation fails to predict how an individual recruits and coordinates their muscles during movement, it will have limited utility for informing design or rehabilitation. While inverse dynamic simulations have previously been used to evaluate anticipated responses from interventions, like orthopedic surgery or orthoses, they frequently struggle to accurately estimate muscle activations, even for tasks like walking. The simulated muscle activity often fails to represent experimentally measured muscle activity from electromyographic (EMG) recordings. Research has theorized that the nervous system may simplify the range of possible activations used during dynamic tasks, by constraining activations to weighted groups of muscles, referred to as muscle synergies. Synergies are altered after neurological injury, such as stroke or cerebral palsy (CP), and may provide a method for improving subject-specific models of neuromuscular control. The aim of this study was to test whether constraining simulation to synergies could improve estimated muscle activations compared to EMG data. We evaluated modeled muscle activations during gait for six typically developing (TD) children and six children with CP. Muscle activations were estimated with: (1) static optimization (SO), minimizing muscle activations squared, and (2) synergy SO (SynSO), minimizing synergy activations squared using the weights identified from EMG data for two to five synergies. While SynSO caused changes in estimated activations compared to SO, the correlation to EMG data was not higher in SynSO than SO for either TD or CP groups. The correlations to EMG were higher in CP than TD for both SO (CP: 0.48, TD: 0.36) and SynSO (CP: 0.46, TD: 0.26 for five synergies). Constraining activations to SynSO caused the simulated muscle stress to increase compared to SO for all individuals, causing a 157% increase with two synergies. These results suggest that constraining simulated activations in inverse dynamic simulations to subject-specific synergies alone may not improve estimation of muscle activations during gait for generic musculoskeletal models.

中文翻译：

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肌肉协同约束不会改善未受损儿童或脑瘫儿童步态期间静态优化对肌肉活动的估计。

神经肌肉骨骼模拟提供了一个有前途的平台，可以为辅助设备的设计或康复提供信息。对于这些应用，模拟必须能够准确地代表感兴趣的人，例如患有神经损伤的人。如果模拟无法预测个体在运动过程中如何募集和协调肌肉，那么它对于指导设计或康复的效用将受到限制。虽然逆动态模拟以前被用来评估干预措施（如整形外科或矫形器）的预期反应，但它们经常难以准确估计肌肉激活，即使对于步行等任务也是如此。模拟的肌肉活动通常无法代表肌电图 (EMG) 记录中实验测量的肌肉活动。研究认为，神经系统可以通过限制对加权肌肉群的激活（称为肌肉协同作用）来简化动态任务期间可能使用的激活范围。神经损伤（例如中风或脑瘫（CP））后协同作用发生改变，并且可能提供一种改善神经肌肉控制的受试者特异性模型的方法。本研究的目的是测试与 EMG 数据相比，将模拟限制为协同作用是否可以改善估计的肌肉激活。我们评估了 6 名典型发育 (TD) 儿童和 6 名 CP 儿童在步态过程中的肌肉激活模型。通过以下方式估计肌肉激活：(1) 静态优化 (SO)，最小化肌肉激活平方，以及 (2) 协同 SO (SynSO)，使用从 EMG 数据确定的 2 到 5 个协同作用的权重最小化协同激活平方。虽然与 SO 相比，SynSO 导致估计激活发生变化，但对于 TD 或 CP 组，SynSO 与 EMG 数据的相关性并不高于 SO。对于SO（CP：0.48，TD：0.36）和SynSO（对于五种协同作用，CP：0.46，TD：0.26），CP 与 EMG 的相关性高于 TD。与所有个体的 SO 相比，限制 SynSO 的激活导致模拟肌肉压力增加，两种协同作用导致模拟肌肉压力增加 157%。这些结果表明，仅将逆动态模拟中的模拟激活限制为特定于受试者的协同作用可能无法改善通用肌肉骨骼模型步态期间肌肉激活的估计。