论文标题
实时步态阶段和任务估计，用于控制极不平衡的脚踝外骨骼
Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain
论文作者
论文摘要
在实验室环境中，已经有较低的LIMB外骨骼报道了积极的生物力学结果，但是这些设备很难与人体步态同步提供适当的辅助，因为在现实世界环境中，相位进程的任务或相位进展变化的速度。本文为脚踝外骨骼提供了一个控制器，该控制器使用数据驱动的运动学模型来连续估计运动过程中的相位，相位速率，步幅长度和地面倾斜状态，从而实现了扭矩辅助的实时适应以匹配在10个能够组合受试者的多动态数据库中观察到的人扭矩。我们在现场实验中与新的10个健全的参与者进行了实时实验证明，控制器得出的阶段估计值与最新的状态相当，同时还估算了与最近的机器学习方法相似的任务变量。在受控的跑步机试验（n = 10，阶段RMSE：4.8 +-2.4 \％）和具有极为不平衡的地形（n = 1，阶段RMSE：4.8 +-2.7 +-2.7 \％）的情况下，实施控制器成功地适应了响应变化的阶段和任务变量（n = 10，阶段RMSE：4.8 + - 2.4 \％），成功地适应了其帮助。
Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 +- 2.4\%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 +- 2.7\%).
