基于惯性测量单元(IMU)的关节角度估计是一种日益成熟的技术,在临床上有着广泛的应用,生物力学和机器人技术。然而,不同IMU参考帧的偏差,参考IMU的各个方向估计误差,由于概念混乱,仍然是提高角度估计精度的挑战,相对简单的度量标准和缺乏系统的调查。在本文中,我们澄清了参照系统一的确定,通过实验研究参考框架偏差的时变特性,并据此提出了一种具有综合度量的新方法来统一参考框架。具体而言,我们首先定义参考框架统一(RFU),并将其与一直与术语RFU混淆的漂移校正区分开。其次,我们设计了一个基于机械万向节的实验来研究偏差,其中排除了传感器到身体的对准和旋转引起的方向差异。第三,根据实验结果,我们提出了一种新颖的方法来利用铰链-关节约束下关节轴的一致性,重力加速度和局部磁场,以全面统一参考系,满足偏差的非线性时变特性。对十个人类受试者的结果揭示了我们提出的方法的可行性以及对以前方法的改进。这项工作有助于考虑和提高基于IMU的关节角度估计的准确性的相对较新的观点。
Inertial measurement unit (IMU)-based joint angle estimation is an increasingly mature technique that has a broad range of applications in clinics, biomechanics and robotics. However, the deviations of different IMUs\' reference frames, referring to IMUs\' individual orientations estimating errors, is still a challenge for improving the angle estimation accuracy due to conceptual confusion, relatively simple metrics and the lack of systematical investigation. In this paper, we clarify the determination of reference frame unification, experimentally study the time-varying characteristics of reference frames\' deviations and accordingly propose a novel method with a comprehensive metric to unify reference frames. To be specific, we firstly define the reference frame unification (RFU) and distinguish it with drift correction that has always been confused with the term RFU. Secondly, we design a mechanical gimbal-based experiment to study the deviations, where sensor-to-body alignment and rotation-caused differences of orientations are excluded. Thirdly, based on the findings of the experiment, we propose a novel method to utilize the consistency of the joint axis under the hinge-joint constraint, gravity acceleration and local magnetic field to comprehensively unify reference frames, which meets the nonlinear time-varying characteristics of the deviations. The results on ten human subjects reveal the feasibility of our proposed method and the improvement from previous methods. This work contributes to a relatively new perspective of considering and improving the accuracy of IMU-based joint angle estimation.