Effects of Personalization on Gait-State Tracking Performance Using Extended Kalman Filters.
Effects of Personalization on Gait-State Tracking Performance Using Extended Kalman Filters.
复制标题
使用扩展卡尔曼滤波器的个性化对步态状态跟踪性能的影响。
DOI：
10.1109/iros55552.2023.10342498
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Gregg,RobertD
中科院分区：
文献类型：
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作者：
Montes-Pérez,JoséA;Thomas,GrayCortright;Gregg,RobertD
新兴的部分辅助外骨骼可以增强健全的表现，并帮助患有病态步态或与年龄相关的不动的人。然而，每个人的行走方式不同，这使得很难从关节运动学直接计算辅助扭矩。基于步态状态估计的控制器使用相位（归一化步幅时间）和任务变量（例如，步幅和地面倾斜度）来参数化关节扭矩。使用依赖于步态状态的运动学模型，先前的工作使用了扩展卡尔曼滤波器（EKF）来在线估计步态状态。然而，这种EKF遭受运动学误差，因为它使用了主体无关的测量模型，它仍然是未知的，如何个性化的测量模型将减少步态状态跟踪误差。本文量化了当使用基于EKF的步态状态估计器时，个性化测量模型相对于主体独立测量模型可以具有多少步态状态跟踪改进。由于EKF的性能取决于测量模型协方差矩阵，我们测试了多个不同的调整参数。在调整参数的合理值，导致良好的性能，个性化改善估计误差平均为8.5 ± 13.8%的相位（平均值±标准差），27.2%的步长，10.5%的地面倾斜度。这些发现支持了以下假设：测量模型的个性化显著提高了基于EKF的步态状态跟踪中的步态状态估计性能，这最终可以对更快的人类步态变化做出可靠的响应。
Emerging partial-assistance exoskeletons can enhance able-bodied performance and aid people with patho-logical gait or age-related immobility. However, every person walks differently, which makes it difficult to directly compute assistance torques from joint kinematics. Gait-state estimation-based controllers use phase (normalized stride time) and task variables (e.g., stride length and ground inclination) to parameterize the joint torques. Using kinematic models that depend on the gait-state, prior work has used an Extended Kalman filter (EKF) to estimate the gait-state online. However, this EKF suffered from kinematic errors since it used a subject-independent measurement model, and it is still unknown how personalization of this measurement model would reduce gait-state tracking error. This paper quantifies how much gait-state tracking improvement a personalized measurement model can have over a subject-independent measurement model when using an EKF-based gait-state estimator. Since the EKF performance depends on the measurement model covariance matrix, we tested on multiple different tuning parameters. Across reasonable values of tuning parameters that resulted in good performance, personalization improved estimation error on average by 8.5 ± 13.8% for phase (mean ± standard deviation), 27.2for stride length, and 10.5for ground inclination. These findings support the hypothesis that personalization of the measurement model significantly improves gait-state estimation performance in EKF based gait-state tracking, which could ultimately enable reliable responses to faster human gait changes.