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.

中文翻译：

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穿戴者与假肢互动的对称步态：由强化学习假肢控制实现的研究。

随着机器人假肢的发展，研究者试图恢复被截肢者的步态表现（例如，步态对称），而不是恢复正常的膝盖运动学/动力学。然而，关于假体力学/控制如何影响穿戴者假肢的步态性能（如步态对称性，稳定性等）的了解很少。本研究旨在研究机器人股骨假体力学对人类穿戴者步态对称性的影响。我们先前设计的强化学习（RL）辅助控制使这项研究成为可能，该辅助控制同时调整了12个控制参数，这些参数确定了整个步态周期的假体力学。RL控制设计有助于在人为干预的情况下安全地探索假体力学。招募受试者并用机器人经股假体在跑步机上行走，而RL控制器调整控制参数。测量姿态时间对称性，步长对称性和双侧前后（AP）脉冲。数据分析表明，机器人膝关节力学的变化导致下肢的运动发生变化，因此导致步态时空对称性测量。在所有受试者中，肢间AP脉冲测量与步态对称性一致：站立时间对称性与净肢间AP脉冲显着相关，步长对称性与制动和推进脉冲对称性显着相关。结果表明，可以个性化经股假体控制，以改善时空步态对称性。然而，仅调整假体力学不足以使步态对称最大化。相反，要实现步态对称，可能需要穿戴者对完整肢体的运动控制与假肢关节的自适应控制之间的协调。结果还表明，基于RL的假体调整系统是研究穿戴者与假体相互作用的潜在工具。