Nogueira Samuel L, Lambrecht Stefan, Inoue Roberto S, Bortole Magdo, Montagnoli Arlindo N, Moreno Juan C, Rocon Eduardo, Terra Marco H, Siqueira Adriano A G, Pons Jose L
Department of Electrical Engineering, Federal University of São Carlos, São Carlos, Brazil.
Division PMA, Department of Mechanical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
Biomed Eng Online. 2017 May 16;16(1):58. doi: 10.1186/s12938-017-0346-7.
In this paper we propose the use of global Kalman filters (KFs) to estimate absolute angles of lower limb segments. Standard approaches adopt KFs to improve the performance of inertial sensors based on individual link configurations. In consequence, for a multi-body system like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link angle estimations (e.g., foot). Global KF approaches, on the other hand, correlate the collective contribution of all signals from lower limb segments observed in the state-space model through the filtering process. We present a novel global KF (matricial global KF) relying only on inertial sensor data, and validate both this KF and a previously presented global KF (Markov Jump Linear Systems, MJLS-based KF), which fuses data from inertial sensors and encoders from an exoskeleton. We furthermore compare both methods to the commonly used local KF.
The results indicate that the global KFs performed significantly better than the local KF, with an average root mean square error (RMSE) of respectively 0.942° for the MJLS-based KF, 1.167° for the matrical global KF, and 1.202° for the local KFs. Including the data from the exoskeleton encoders also resulted in a significant increase in performance.
The results indicate that the current practice of using KFs based on local models is suboptimal. Both the presented KF based on inertial sensor data, as well our previously presented global approach fusing inertial sensor data with data from exoskeleton encoders, were superior to local KFs. We therefore recommend to use global KFs for gait analysis and exoskeleton control.
在本文中，我们提出使用全局卡尔曼滤波器（KF）来估计下肢各节段的绝对角度。标准方法采用卡尔曼滤波器，根据各个环节的配置来提高惯性传感器的性能。因此，对于像下肢外骨骼这样的多体系统，一个环节（例如小腿）的惯性测量在其他环节角度估计（例如足部）中未被考虑。另一方面，全局卡尔曼滤波器方法通过滤波过程，在状态空间模型中关联下肢各节段所有信号的集体贡献。我们提出了一种仅依赖惯性传感器数据的新型全局卡尔曼滤波器（矩阵全局卡尔曼滤波器），并对该卡尔曼滤波器和先前提出的全局卡尔曼滤波器（基于马尔可夫跳跃线性系统，MJLS的卡尔曼滤波器）进行了验证，后者融合了来自惯性传感器和外骨骼编码器的数据。此外，我们还将这两种方法与常用的局部卡尔曼滤波器进行了比较。
结果表明，全局卡尔曼滤波器的性能明显优于局部卡尔曼滤波器，基于MJLS的卡尔曼滤波器平均均方根误差（RMSE）分别为0.942°，矩阵全局卡尔曼滤波器为1.167°，局部卡尔曼滤波器为1.202°。包含外骨骼编码器的数据也显著提高了性能。
结果表明，当前基于局部模型使用卡尔曼滤波器的做法并非最优。所提出的基于惯性传感器数据的卡尔曼滤波器以及我们先前提出的将惯性传感器数据与外骨骼编码器数据融合的全局方法均优于局部卡尔曼滤波器。因此，我们建议在步态分析和外骨骼控制中使用全局卡尔曼滤波器。
