Imposing Healthy Hip Motion Pattern and Range by Exoskeleton Control for Individualized Assistance
Imposing Healthy Hip Motion Pattern and Range by Exoskeleton Control for Individualized Assistance
复制标题
利用外骨骼控制施加健康的髋关节运动模式和范围以实现个性化辅助
DOI：
10.1109/lra.2022.3196105
复制
发表时间：
2022-10
影响因子：
5.2
通讯作者：
Qiang Zhang;Varun Nalam;Xikai Tu;Minhan Li;Jennie Si;M. Lewek;H. Huang
中科院分区：
文献类型：
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作者：
Qiang Zhang;Varun Nalam;Xikai Tu;Minhan Li;Jennie Si;M. Lewek;H. Huang
动力外骨骼是一种很有前途的设备，可以改善神经损伤患者的行走方式。然而，由于人机交互的不确定性和时变性质，提供个性化的外部援助是具有挑战性的。最近，人在环优化（human-in-the-loop， HIL）被研究用于提供最小化能量消耗的帮助，通常用代谢成本来量化。然而，这种全身整体效应评估可能不能直接反映目标关节的局部功能。这使得评估机器人辅助时的直接效果变得困难。此外，HIL优化方法通常不考虑局部关节轨迹，而这一考虑对于下肢运动缺陷患者施加健康的关节运动和步态模式非常重要。在本文中，我们提出了一个基于无模型强化学习（RL）的控制框架，以实现行走过程中髋关节的规范运动范围和步态模式。我们基于rl的控制提供个性化的辅助扭矩配置，通过启发式地操纵三个控制参数，分别在行走过程中髋关节屈曲和伸展。设计了最小二乘策略迭代，通过调整控制参数来优化与控制努力和髋关节轨迹误差相关的成本函数。为了评估设计方法的性能，使用压缩套筒来约束未受损人类参与者的髋关节来模拟运动缺陷。所提出的RL控制成功地实现了在跑步机上行走的三名参与者扩大髋关节活动范围的预期目标。
Powered exoskeletons are promising devices to improve the walking patterns of people with neurological impairments. Providing personalized external assistance though is challenging due to uncertainties and the time-varying nature of human-robot interaction. Recently, human-in-the-loop (HIL) optimization has been investigated for providing assistance to minimize energetic expenditure, usually quantified by metabolic cost. However, this full-body global effect evaluation may not directly reflect the local functions of the targeted joint(s). This makes it difficult to assess the direct effect when robotic assistance is provided. In addition, the HIL optimization method usually does not take into account local joint trajectories, a consideration that is important in imposing healthy joint movements and gait patterns for individuals with lower limb motor deficits. In this paper, we propose a model-free reinforcement learning (RL)-based control framework to achieve a normative range of motion and gait pattern of the hip joint during walking. Our RL-based control provides personalized assistance torque profile by heuristically manipulating three control parameters for hip flexion and extension, respectively, during walking. A least square policy iteration was devised to optimize a cost function associated with control efforts and hip joint trajectory errors by tuning the control parameters. To evaluate the performance of the design approach, a compression sleeve was used to constrain the hip joint of unimpaired human participants to simulate motor deficits. The proposed RL control successfully achieved the desired goal of enlarging the hip joint's range of motion in three participants walking on a treadmill.