LABLAB, Department of Human Movement and Sports Sciences, University of Rome Foro Italico, Rome, Italy.
J Neuroeng Rehabil. 2013 Mar 11;10:29. doi: 10.1186/1743-0003-10-29.
The present study aimed at devising a data processing procedure that provides an optimal estimation of the 3-D instantaneous orientation of an inertial measurement unit (IMU). This result is usually obtained by fusing the data measured with accelerometers, gyroscopes, and magnetometers. Nevertheless, issues related to compensation of integration errors and high sensitivity of these devices to magnetic disturbances call for different solutions. In this study, a method based on the use of gyroscope data only is presented, which uses a Weighted Fourier Linear Combiner adaptive filter to perform a drift-free estimate of the 3D orientation of an IMU located on the lower trunk during walking.
A tuning of the algorithm parameters and a sensitivity analysis to its initial conditions was performed using treadmill walking data from 3 healthy subjects. The accuracy of the method was then assessed using data collected from 15 young healthy subjects during treadmill walking at variable speeds and comparing the pitch, roll, and yaw angles estimated from the gyroscopes data to those obtained with a stereophotogrammetric system. Root mean square (RMS) difference and correlation coefficients (r) were used for this purpose.
An optimal set of values of the algorithm parameters was established. At all the observed speeds, and also in all the various sub-phases, the investigated angles were all estimated to within an average RMS difference of less than 1.2 deg and an average r greater than 0.90.
This study proved the effectiveness of the Weighted Fourier Linear Combiner method in accurately reconstructing the 3D orientation of an IMU located on the lower trunk of a subject during treadmill walking. This method is expected to also perform satisfactorily for overground walking data and to be applicable also to other "quasi-periodic" tasks, such as squatting, rowing, running, or swimming.
本研究旨在设计一种数据处理程序，以最佳估计惯性测量单元 (IMU) 的 3-D 瞬时方向。这一结果通常是通过融合加速度计、陀螺仪和磁力计测量的数据来实现的。然而，与积分误差补偿以及这些设备对磁干扰的高敏感性相关的问题需要不同的解决方案。在本研究中，提出了一种仅基于使用陀螺仪数据的方法，该方法使用加权傅里叶线性组合自适应滤波器来执行位于行走时下躯干的 IMU 的 3D 方向的无漂移估计。
使用来自 3 名健康受试者的跑步机行走数据，对算法参数进行调整和对其初始条件进行敏感性分析。然后，使用来自 15 名年轻健康受试者在跑步机上以不同速度行走时收集的数据，通过比较从陀螺仪数据估计的俯仰、横滚和偏航角与使用立体摄影测量系统获得的角度，评估该方法的准确性。为此，使用均方根 (RMS) 差和相关系数 (r)。
确定了一组最优的算法参数值。在所有观察到的速度下，以及在所有不同的子阶段中，所研究的角度的估计值的平均 RMS 差均小于 1.2 度，平均 r 大于 0.90。
这项研究证明了加权傅里叶线性组合方法在准确重建位于跑步机行走受试者下躯干的 IMU 的 3D 方向方面的有效性。该方法有望在地面行走数据中也能表现良好，并且适用于其他“准周期性”任务，如深蹲、划船、跑步或游泳。
