User gait phase estimation plays a key role for the seamless control of the lower-limb robotic assistive devices (e.g., exoskeletons or prostheses) during ambulation. To achieve this, several studies have attempted to estimate the gait phase using a thigh or shank angle. However, their estimation resulted in some deviation from the actual walking and varied across the walking speeds. In this study, we investigated the different setups using for the machine learning approach to obtain more accurate and consistent gait phase estimation for the robotic transfemoral prosthesis over different walking speeds. Considering the transfemoral prosthetic application, we proposed two different sensor setups: i) the angular positions and velocities of both thigh and torso (S1) and ii) the angular positions and velocities of both thigh and torso, and heel force data (S2). The proposed setups and method are experimentally evaluated with three healthy young subjects at four different walking speeds: 0.5, 1.0, 1.5, and 2.0 m/s. Both results showed robust and accurate gait phase estimation with respect to the ground truth (loss value of S1: 4.54e-03 Vs. S2: 4.70e-03). S1 had the advantage of a simple equipment setup using only two IMUs, while S2 had the advantage of estimating more accurate heel-strikes than S1 by using additional heel force data. The choice between the two sensor setups can depend on the researchers’ preference in consideration of the device setup or the focus of the interest.

中文翻译：

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使用 LSTM 进行机器人跨股假肢跨步行速度的连续步态相位估计


用户步态相位估计对于行走过程中下肢机器人辅助设备（例如外骨骼或假肢）的无缝控制起着关键作用。为了实现这一目标，一些研究尝试使用大腿或小腿角度来估计步态阶段。然而，他们的估计与实际步行存在一些偏差，并且随着步行速度的不同而变化。在这项研究中，我们研究了用于机器学习方法的不同设置，以获得机器人经股假肢在不同步行速度下更准确和一致的步态相位估计。考虑到经股假肢应用，我们提出了两种不同的传感器设置：i）大腿和躯干的角位置和速度（S1）和ii）大腿和躯干的角位置和速度以及脚跟力数据（S2）。所提出的设置和方法通过三名健康的年轻受试者以四种不同的步行速度进行实验评估：0.5、1.0、1.5 和 2.0 m/s。两个结果都显示了相对于真实情况的稳健且准确的步态相位估计（S1 的损失值：4.54e-03 与 S2：4.70e-03）。 S1 的优点是设备设置简单，仅使用两个 IMU，而 S2 的优点是通过使用额外的脚跟力数据，可以比 S1 估计更准确的脚跟着地。两种传感器设置之间的选择取决于研究人员考虑设备设置或兴趣焦点的偏好。