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穿戴式假体交互作用的对称步态:强化学习控制假体的研究。

Wearer-Prosthesis Interaction for Symmetrical Gait: A Study Enabled by Reinforcement Learning Prosthesis Control.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2020 Apr;28(4):904-913. doi: 10.1109/TNSRE.2020.2979033. Epub 2020 Mar 9.

Abstract

With advances in robotic prostheses, rese-archers attempt to improve amputee's gait performance (e.g., gait symmetry) beyond restoring normative knee kinematics/kinetics. Yet, little is known about how the prosthesis mechanics/control influence wearer-prosthesis' gait performance, such as gait symmetry, stability, etc. This study aimed to investigate the influence of robotic transfemoral prosthesis mechanics on human wearers' gait symmetry. The investigation was enabled by our previously designed reinforcement learning (RL) supplementary control, which simultaneously tuned 12 control parameters that determined the prosthesis mechanics throughout a gait cycle. The RL control design facilitated safe explorations of prosthesis mechanics with the human in the loop. Subjects were recruited and walked with a robotic transfemoral prosthesis on a treadmill while the RL controller tuned the control parameters. Stance time symmetry, step length symmetry, and bilateral anteroposterior (AP) impulses were measured. The data analysis showed that changes in robotic knee mechanics led to movement variations in both lower limbs and therefore gait temporal-spatial symmetry measures. Consistent across all the subjects, inter-limb AP impulse measurements explained gait symmetry: the stance time symmetry was significantly correlated with the net inter-limb AP impulse, and the step length symmetry was significantly correlated with braking and propulsive impulse symmetry. The results suggest that it is possible to personalize transfemoral prosthesis control for improved temporal-spatial gait symmetry. However, adjusting prosthesis mechanics alone was insufficient to maximize the gait symmetry. Rather, achieving gait symmetry may require coordination between the wearer's motor control of the intact limb and adaptive control of the prosthetic joints. The results also indicated that the RL-based prosthesis tuning system was a potential tool for studying wearer-prosthesis interactions.

摘要

随着机器人假肢技术的进步,研究人员试图超越恢复正常膝关节运动学/动力学的目标,进一步提高截肢者的步态性能(例如,步态对称性)。然而,对于假肢力学/控制如何影响佩戴者-假肢的步态性能(例如步态对称性、稳定性等),我们知之甚少。本研究旨在探讨机器人仿生膝关节假肢力学对人类佩戴者步态对称性的影响。本研究通过我们之前设计的强化学习(RL)补充控制来实现,该控制同时调整了 12 个控制参数,这些参数决定了整个步态周期内的假肢力学。RL 控制设计促进了在人体闭环中对假肢力学的安全探索。研究对象在跑步机上佩戴机器人仿生膝关节假肢行走,同时 RL 控制器调整控制参数。测量了站立时间对称性、步长对称性和双侧前后向(AP)冲量。数据分析表明,机器人膝关节力学的变化导致了下肢的运动变化,从而导致了步态时间-空间对称性测量的变化。所有研究对象的结果一致,肢体间的 AP 冲量测量结果解释了步态对称性:站立时间对称性与净肢体间 AP 冲量显著相关,步长对称性与制动和推进冲量对称性显著相关。结果表明,为了提高时间-空间步态对称性,可以对仿生膝关节假肢控制进行个性化设置。然而,仅调整假肢力学不足以最大限度地提高步态对称性。相反,实现步态对称性可能需要佩戴者对健全肢体的运动控制和假肢关节的自适应控制之间的协调。研究结果还表明,基于 RL 的假肢调整系统是研究佩戴者-假肢相互作用的潜在工具。

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