State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Shaanxi Key Laboratory of Intelligent Robots and School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2020 Sep 18;20(18):5357. doi: 10.3390/s20185357.
In this work, we propose an intuitive and real-time model of the human arm active endpoint stiffness. In our model, the symmetric and positive-definite stiffness matrix is constructed through the eigendecomposition Kc=VDVT, where V is an orthonormal matrix whose columns are the normalized eigenvectors of Kc, and D is a diagonal matrix whose entries are the eigenvalues of Kc. In this formulation, we propose to construct V and D directly by exploiting the geometric information from a reduced human arm skeleton structure in 3D and from the assumption that human arm muscles work synergistically when co-contracted. Through the perturbation experiments across multiple subjects under different arm configurations and muscle activation states, we identified the model parameters and examined the modeling accuracy. In comparison to our previous models for predicting human active arm endpoint stiffness, the new model offers significant advantages such as fast identification and personalization due to its principled simplicity. The proposed model is suitable for applications such as teleoperation, human-robot interaction and collaboration, and human ergonomic assessments, where a personalizable and real-time human kinodynamic model is a crucial requirement.
在这项工作中，我们提出了一种直观的、实时的人类手臂主动端点刚度模型。在我们的模型中，通过特征分解 Kc=VDVT 构建对称正定的刚度矩阵，其中 V 是一个正交矩阵，其列是 Kc 的归一化特征向量，D 是一个对角矩阵，其条目是 Kc 的特征值。在这种表述中，我们建议通过利用 3D 简化的人类手臂骨骼结构的几何信息以及人类手臂肌肉在共同收缩时协同工作的假设，直接构建 V 和 D。通过在不同手臂配置和肌肉激活状态下对多个受试者进行的摄动实验，我们确定了模型参数并检验了建模精度。与我们之前用于预测人类主动手臂端点刚度的模型相比，由于其原理简单，新模型具有快速识别和个性化的优势。所提出的模型适用于遥操作、人机交互和协作以及人体工效评估等应用，在这些应用中，可个性化和实时的人体运动动力学模型是一个关键要求。
