Formica Domenico, Azhar Muhammad, Tommasino Paolo, Campolo Domenico
IEEE Int Conf Rehabil Robot. 2019 Jun;2019:151-156. doi: 10.1109/ICORR.2019.8779380.
Estimating joint stiffness is of paramount importance for studying human motor control and for clinical assessment of neurological diseases. Usually stiffness estimation is performed using cumbersome instrumentations (e.g. robots), and by approximating robot joint angles and torques to the human ones. This paper proposes a methodology and an experimental setup to measure wrist joint stiffness in unstructured environments, with the twofold aim of: 1) providing a geometric framework in order to derive angular displacements and torques at the wrist Flexion/Extension (FE) and Radial/Ulnar Deviation (RUD) axes of rotation, using a subject specific kinematic model; 2) suggesting an experimental setup made of two portable sensors for motion tracking and one load cell, to allow for measurements in out-of-the-lab scenarios. We tested our method on a hardware mockup of wrist kinematics, providing a ground truth for estimated angles and torques at FE and RUD joints. The experimental validation showed average absolute errors in FE and RUD angles of 0.005 rad and 0.0167 rad respectively, and an average error of FE and RUD torques of 0.006 Nm and 0.003 Nm.
估计关节刚度对于研究人体运动控制和神经系统疾病的临床评估至关重要。通常，刚度估计是使用笨重的仪器（如机器人），并通过将机器人关节角度和扭矩近似为人体关节角度和扭矩来进行的。本文提出了一种方法和实验装置，用于在非结构化环境中测量腕关节刚度，其双重目标是：1）提供一个几何框架，以便使用特定于个体的运动学模型，得出腕关节屈伸（FE）和桡尺偏斜（RUD）旋转轴上的角位移和扭矩；2）提出一种由两个用于运动跟踪的便携式传感器和一个测力传感器组成的实验装置，以允许在实验室外的场景中进行测量。我们在腕关节运动学的硬件模型上测试了我们的方法，为FE和RUD关节处的估计角度和扭矩提供了基准真值。实验验证表明，FE和RUD角度的平均绝对误差分别为0.005弧度和0.0167弧度，FE和RUD扭矩的平均误差分别为0.006牛米和0.003牛米。
