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) (CCI1) and Falconer and Winter (1985) (CCI2). CCI1 measures antagonist muscle activation relative to not only total activation of agonist plus antagonist muscles but also agonist muscle activation, while CCI2 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) CCI1 was generally more correlated with joint stiffness than was CCI2, (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.

中文翻译：

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中风后个体在步态期间常用的联合收缩指数如何近似下肢关节僵硬趋势？

肌肉共同收缩产生关节僵硬，以提高肢体运动过程中的稳定性和准确性，但代价是更高的能量消耗。然而，使用实验或计算方法很难量化关节刚度。相比之下，使用基于 EMG 的共同收缩指数 (CCI) 对肌肉共同收缩进行量化更容易，并且可能为估计关节刚度提供替代方案。本研究调查了在步态过程中使用两种常见 CCI 来近似下肢关节僵硬趋势的可行性。为两个人中风后构建的校准 EMG 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数量。基于两种常见的 CCI 公式计算拮抗肌对的各种组合的 CCI：Rudolph 等。(2000) (CCI1) 和 Falconer 和 Winter (1985) (CCI2)。CCI1 测量拮抗肌激活，不仅与激动肌和拮抗肌的总激活相关，而且与激动肌激活相关，而 CCI2 测量拮抗肌激活仅与总肌肉激活相关。我们计算了这两个 CCI 与基于模型的双腿髋关节、膝关节和踝关节矢状面关节刚度估计值之间的相关性。尽管我们观察到一些 CCI 公式与相应的关节刚度之间存在中到强的相关性，但这些关联高度依赖于为 CCI 计算所做的方法选择。具体来说，我们发现：(1) CCI1 通常比 CCI2 与关节僵硬更相关，(2) 使用带有校准机电延迟的 EMG 信号计算 CCI 通常产生与关节僵硬的最佳相关性，以及 (3) 拮抗肌对的选择显着影响 CCI 与关节僵硬的相关性关节僵硬。通过提供有关方法选择如何影响 CCI 与关节刚度趋势的相关性的指导，这项研究可能会促进一种更简单的替代方法来研究人体运动过程中的关节刚度。