Mechanical impedance, which changes with posture and muscle activations, characterizes how the central nervous system regulates the interaction with the environment. Traditional approaches to impedance estimation, based on averaging of movement kinetics, requires a large number of trials and may introduce bias to the estimation due to the high variability in a repeated or periodic movement. Here, we introduce a data-driven modeling technique to estimate joint impedance considering the large gait variability. The proposed method can be used to estimate impedance in both the stance and swing phases of walking. A 2-pass clustering approach is used to extract groups of unperturbed gait data and estimate candidate baselines. Then patterns of perturbed data are matched with the most similar unperturbed baseline. The kinematic and torque deviations from the baselines are regressed locally to compute joint impedance at different gait phases. Simulations using the trajectory data of a subject's gait at different speeds demonstrate a more accurate estimation of ankle stiffness and damping with the proposed clustering-based method when compared with two methods: i) using average unperturbed baselines, and ii) matching shifted and scaled average unperturbed velocity baselines. Furthermore, the proposed method requires fewer trials than methods based on average unperturbed baselines. The experimental results on human hip impedance estimation show the feasibility of clustering-based technique and verifies that it reduces the estimation variability.

中文翻译：

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基于聚类的步行过程中关节阻抗识别方法


机械阻抗随姿势和肌肉激活而变化，表征中枢神经系统如何调节与环境的相互作用。基于运动动力学平均的传统阻抗估计方法需要大量试验，并且由于重复或周期性运动的高度可变性可能会给估计带来偏差。在这里，我们引入了一种数据驱动的建模技术来估计关节阻抗，考虑到大的步态变异性。所提出的方法可用于估计行走的站立阶段和摆动阶段的阻抗。使用 2 遍聚类方法来提取未受干扰的步态数据组并估计候选基线。然后将受扰动数据的模式与最相似的未受扰动基线进行匹配。与基线的运动学和扭矩偏差进行局部回归，以计算不同步态阶段的关节阻抗。使用不同速度下受试者步态的轨迹数据进行的模拟表明，与以下两种方法相比，所提出的基于聚类的方法可以更准确地估计脚踝刚度和阻尼：i）使用平均未扰动基线，ii）匹配移动和缩放平均值不受干扰的速度基线。此外，所提出的方法比基于平均未扰动基线的方法需要更少的试验。人体髋部阻抗估计的实验结果表明了基于聚类的技术的可行性，并验证了它减少了估计的变异性。