Laboratorio di Tecnologia Medica, Istituto Ortopedico Rizzoli, Bologna, Italy.
J Biomech. 2011 Jun 3;44(9):1716-21. doi: 10.1016/j.jbiomech.2011.03.039. Epub 2011 Apr 16.
Skeletal forces are fundamental information in predicting the risk of bone fracture. The neuromotor control system can drive muscle forces with various task- and health-dependent strategies but current modelling techniques provide a single optimal solution of the muscle load sharing problem. The aim of the present work was to study the variability of the hip load magnitude due to sub-optimal neuromotor control strategies using a subject-specific musculoskeletal model. The model was generated from computed tomography (CT) and dissection data from a single cadaver. Gait kinematics, ground forces and electromyographic (EMG) signals were recorded on a body-matched volunteer. Model results were validated by comparing the traditional optimisation solution with the published hip load measurements and the recorded EMG signals. The solution space of the instantaneous equilibrium problem during the first hip load peak resulted in 10(5) dynamically equivalent configurations of the neuromotor control. The hip load magnitude was computed and expressed in multiples of the body weight (BW). Sensitivity of the hip load boundaries to the uncertainty on the muscle tetanic stress (TMS) was also addressed. The optimal neuromotor control induced a hip load magnitude of 3.3 BW. Sub-optimal neuromotor controls induced a hip load magnitude up to 8.93 BW. Reducing TMS from the maximum to the minimum the lower boundary of the hip load magnitude varied moderately whereas the upper boundary varied considerably from 4.26 to 8.93 BW. Further studies are necessary to assess how far the neuromotor control can degrade from the optimal activation pattern and to understand which sub-optimal controls are clinically plausible. However we can consider the possibility that sub-optimal activations of the muscular system play a role in spontaneous fractures not associated with falls.
骨骼力量是预测骨折风险的基本信息。神经运动控制系统可以用各种与任务和健康相关的策略来驱动肌肉力量,但目前的建模技术提供了肌肉负荷分配问题的单一最佳解决方案。本工作的目的是使用基于个体的肌肉骨骼模型研究由于神经运动控制策略不佳而导致的髋关节负荷大小的可变性。该模型是根据一个尸体的 CT 和解剖数据生成的。运动学、地面力和肌电图(EMG)信号是在与身体匹配的志愿者上记录的。通过将传统优化解决方案与发表的髋关节负荷测量值和记录的 EMG 信号进行比较,对模型结果进行了验证。在第一次髋关节负荷峰值期间的瞬时平衡问题的解空间导致了 10(5) 个神经运动控制的动态等效配置。计算了髋关节负荷大小,并以体重(BW)的倍数表示。还研究了髋关节负荷边界对肌肉强直收缩(TMS)不确定性的敏感性。最佳神经运动控制导致髋关节负荷大小为 3.3 BW。神经运动控制不佳导致髋关节负荷大小高达 8.93 BW。将 TMS 从最大值降低到最小值,髋关节负荷大小的下限变化适中,而上限从 4.26 变化到 8.93 BW 变化很大。需要进一步研究,以评估神经运动控制从最佳激活模式退化的程度,以及了解哪些次优控制在临床上是合理的。然而,我们可以考虑肌肉系统的次优激活在与跌倒无关的自发性骨折中发挥作用的可能性。