Robust and Adaptive Lower Limb Prosthesis Stance Control via Extended Kalman Filter-Based Gait Phase Estimation
Robust and Adaptive Lower Limb Prosthesis Stance Control via Extended Kalman Filter-Based Gait Phase Estimation
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DOI：
10.1109/lra.2019.2924841
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发表时间：
2019-10-01
影响因子：
5.2
通讯作者：
Geyer, Hartmut
中科院分区：
文献类型：
--
作者：
Thatte, Nitish;Shah, Tanvi;Geyer, Hartmut
我们提出了一种控制策略的动力假肢的基础上，用于确定适当的控制动作的步态相位的鲁棒性估计。我们使用扩展卡尔曼滤波器（EKF），融合关节角度和速度测量来估计步态相位，我们在这项工作中定义为一个变量，在脚跟罢工从零到脚趾关闭的一个立场不断进步。该控制策略使用步态相位估计作为高斯过程（GP）函数的输入，该高斯过程函数指定膝关节和踝关节的期望角度、速度和前馈扭矩。我们比较了这一建议的GP-EKF控制策略的两个替代控制器：神经肌肉（NM）控制策略，腿部肌肉模型和假设的反射，和阻抗（IMP）控制策略。我们的实验涉及七名身体健全的参与者和一名截肢者。我们发现，GP-EKE控制产生的膝关节角度轨迹显着更接近健全的步行数据比那些由NM或IMP控制器。然而，踝关节轨迹不太相似。此外，我们发现，在实验中与参与者踩在块在立场，GP-EKE控制导致了显着减少跌倒样事件比IMP控制。最后，我们评估所提出的控制跟踪步态相位在缓慢和快速变化的跑步机速度的能力，并发现EK RS相位估计跟踪这些步态变化显着优于基于时间的相位估计。所提出的控制策略可以提供一个强大的和自适应的控制替代动力假肢。
We present a control strategy for powered prostheses based on a robust estimate of the gait phase that is used to determine appropriate control actions. We use an extended Kalman filter (EKF) that fuses joint angle and velocity measurements to estimate the gait phase, which we define in this work to be a variable that progresses continuously during stance from zero at heel strike to one at toe-off. The control strategy uses the gait phase estimate as the input into Gaussian process (GP) functions that specify the desired angles, velocities, and feed-forward torques for the knee and ankle joints. We compare this proposed GP-EKF control strateu to two alternative controllers: A neuromuscular (NM) control strategy, which models leg muscles and hypothesized reflexes, and an impedance (IMP) control strategy. Our experiments involved seven able-bodied participants and a single amputee participant. We find that the GP-EKE control generated knee angle trajectories that were significantly closer to able-bodied walking data than those produced by either the NM or IMP controllers. However, ankle trajectories were less similar. In addition, we find in experiments with the participants stepping on blocks during stance that the GP-EKE control resulted in significantly fewer fall-like events than IMP control. Finally, we evaluate the ability of the proposed control to track the gait phase across both slowly and rapidly varying treadmill speeds and find that the EK rs phase estimate tracked these gait changes significantly better than a time-based phase estimate. The proposed control strategy may provide a robust and adaptive control alternative for powered prostheses.