Stable and efficient coordination in physical human-robot interaction requires consideration of human feedback behavior. In unpredictable tasks, where voluntary cognitive feedback is too slow to guarantee desired task execution, the human must rely on involuntary intrinsic and reflexive feedback. The combined effects of these two feedback mechanisms and the inertial characteristics can be summarized in the involuntary impedance components. In this work, we present a method for the estimation of the involuntary impedance components of the human arm in multi-joint movements. We apply force perturbations to evoke feedback jerks that can be isolated using a high pass filter and limit the duration of the estimation interval to guarantee exclusion of voluntary cognitive feedback. Dynamic regressor representation of the rigid body dynamics of the arm and first order Taylor series expansion of the feedback behavior yield a model that is linear in the involuntary impedance components. The constant values of the inertial parameters are estimated in a static posture maintenance task and subsequently inserted to estimate the remaining components in a dynamic movement task. The method is validated with simulated data of a neuromechanical model of the human arm and its performance is compared to established methods from the literature. The results of the validation demonstrate superior estimation performance for moderate movement velocities, and less influence of the variability of the movements. The applicability to real data and the plausibility of the limited estimation interval are successfully demonstrated in an experiment with human participants.

中文翻译：

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通过反馈加加速度隔离估计多关节运动中人体手臂阻抗的非自愿分量

物理人机交互中稳定有效的协调需要考虑人类的反馈行为。在不可预测的任务中，自愿认知反馈太慢而无法保证预期的任务执行，人类必须依赖非自愿的内在和反射性反馈。这两种反馈机制和惯性特性的组合效应可以总结为无意识阻抗分量。在这项工作中，我们提出了一种估计多关节运动中人体手臂的无意识阻抗分量的方法。我们应用力扰动来唤起可以使用高通滤波器隔离的反馈抖动，并限制估计间隔的持续时间以保证排除自愿认知反馈。手臂刚体动力学的动态回归量表示和反馈行为的一阶泰勒级数展开产生一个模型，该模型在无意识阻抗分量中是线性的。惯性参数的常数值在静态姿势维护任务中被估计，随后被插入以估计动态运动任务中的剩余分量。该方法通过人体手臂神经力学模型的模拟数据进行验证，并将其性能与文献中建立的方法进行比较。验证结果表明，对于中等运动速度具有优越的估计性能，并且运动变异性的影响较小。