Li Geng, Shourijeh Mohammad S, Ao Di, Patten Carolynn, Fregly Benjamin J
Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States.
Biomechanics, Rehabilitation, and Integrative Neuroscience Lab, Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, Davis, CA, United States.
Front Bioeng Biotechnol. 2021 Jan 7;8:588908. doi: 10.3389/fbioe.2020.588908. eCollection 2020.
Muscle co-contraction generates joint stiffness to improve stability and accuracy during limb movement but at the expense of higher energetic cost. However, quantification of joint stiffness is difficult using either experimental or computational means. In contrast, quantification of muscle co-contraction using an EMG-based Co-Contraction Index (CCI) is easier and may offer an alternative for estimating joint stiffness. This study investigated the feasibility of using two common CCIs to approximate lower limb joint stiffness trends during gait. Calibrated EMG-driven lower extremity musculoskeletal models constructed for two individuals post-stroke were used to generate the quantities required for CCI calculations and model-based estimation of joint stiffness. CCIs were calculated for various combinations of antagonist muscle pairs based on two common CCI formulations: Rudolph et al. (2000) ( ) and Falconer and Winter (1985) ( ). measures antagonist muscle activation relative to not only total activation of agonist plus antagonist muscles but also agonist muscle activation, while measures antagonist muscle activation relative to only total muscle activation. We computed the correlation between these two CCIs and model-based estimates of sagittal plane joint stiffness for the hip, knee, and ankle of both legs. Although we observed moderate to strong correlations between some CCI formulations and corresponding joint stiffness, these associations were highly dependent on the methodological choices made for CCI computation. Specifically, we found that: (1) was generally more correlated with joint stiffness than was , (2) CCI calculation using EMG signals with calibrated electromechanical delay generally yielded the best correlations with joint stiffness, and (3) choice of antagonist muscle pairs significantly influenced CCI correlation with joint stiffness. By providing guidance on how methodological choices influence CCI correlation with joint stiffness trends, this study may facilitate a simpler alternate approach for studying joint stiffness during human movement.
肌肉共同收缩会产生关节刚度,以在肢体运动过程中提高稳定性和准确性,但代价是能量消耗更高。然而,无论是使用实验方法还是计算方法,对关节刚度进行量化都很困难。相比之下,使用基于肌电图的共同收缩指数(CCI)对肌肉共同收缩进行量化则更容易,并且可能为估计关节刚度提供一种替代方法。本研究调查了使用两种常见的CCI来近似步态期间下肢关节刚度趋势的可行性。为两名中风后个体构建的经过校准的肌电图驱动的下肢肌肉骨骼模型,用于生成CCI计算和基于模型的关节刚度估计所需的量。基于两种常见的CCI公式,针对拮抗肌对的各种组合计算CCI:鲁道夫等人(2000年)( )和法尔科纳与温特(1985年)( )。 不仅测量拮抗肌激活相对于主动肌加拮抗肌总激活的情况,还测量相对于主动肌激活的情况,而 仅测量拮抗肌激活相对于总肌肉激活的情况。我们计算了这两种CCI与双腿髋、膝和踝关节矢状面关节刚度的基于模型估计值之间的相关性。尽管我们观察到一些CCI公式与相应关节刚度之间存在中度至强相关性,但这些关联高度依赖于CCI计算所采用的方法选择。具体而言,我们发现:(1) 通常比 与关节刚度的相关性更强,(2)使用具有校准机电延迟的肌电信号进行CCI计算通常与关节刚度具有最佳相关性,(3)拮抗肌对的选择显著影响CCI与关节刚度的相关性。通过提供关于方法选择如何影响CCI与关节刚度趋势相关性的指导,本研究可能有助于为研究人体运动期间的关节刚度提供一种更简单的替代方法。