PDF(Pubmed)
Abstract：
The workflow to simulate motion with recorded data usually starts with selecting a generic musculoskeletal model and scaling it to represent subject-specific characteristics. Simulating muscle dynamics with muscle-tendon parameters computed from existing scaling methods in literature, however, yields some inconsistencies compared to measurable outcomes. For instance, simulating fiber lengths and muscle excitations during walking with linearly scaled parameters does not resemble established patterns in the literature. This study presents a tool that leverages reported in vivo experimental observations to tune muscle-tendon parameters and evaluates their influence in estimating muscle excitations and metabolic costs during walking. From a scaled generic musculoskeletal model, we tuned optimal fiber length, tendon slack length, and tendon stiffness to match reported fiber lengths from ultrasound imaging and muscle passive force-length relationships to match reported in vivo joint moment-angle relationships. With tuned parameters, muscle contracted more isometrically, and soleus\'s operating range was better estimated than with linearly scaled parameters. Also, with tuned parameters, on/off timing of nearly all muscles\' excitations in the model agreed with reported electromyographic signals, and metabolic rate trajectories varied significantly throughout the gait cycle compared to linearly scaled parameters. Our tool, freely available online, can customize muscle-tendon parameters easily and be adapted to incorporate more experimental data.
摘要：
使用记录的数据模拟运动的工作流程通常从选择通用的肌肉骨骼模型并对其进行缩放以表示特定于受试者的特征开始。用文献中现有的缩放方法计算的肌腱参数模拟肌肉动力学，然而,与可衡量的结果相比，会产生一些不一致的地方。例如,用线性缩放参数模拟步行过程中的纤维长度和肌肉兴奋与文献中的既定模式不同。这项研究提供了一种工具，该工具利用已报告的体内实验观察结果来调整肌肉肌腱参数，并评估其在估计步行过程中肌肉兴奋和代谢成本方面的影响。从缩放的通用肌肉骨骼模型中，我们调整了最佳的纤维长度，肌腱松弛长度,和肌腱刚度，以匹配从超声成像报告的纤维长度和肌肉被动力-长度关系，以匹配报告的体内关节力矩-角度关系。使用调整后的参数，肌肉收缩得更等距，和比目鱼的工作范围比线性缩放参数更好地估计。此外，使用调整后的参数，模型中几乎所有肌肉兴奋的开/关时间与报告的肌电信号一致，与线性缩放参数相比，整个步态周期中的代谢率轨迹变化很大。我们的工具,免费在线提供，可以自定义的肌肉肌腱参数容易和适应纳入更多的实验数据。
