Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
Robotics Institute, University of Michigan, Ann Arbor, MI, USA.
Sci Robot. 2022 Mar 30;7(64):eabj3487. doi: 10.1126/scirobotics.abj3487.
User preference is a promising objective for the control of robotic exoskeletons because it may capture the multifactorial nature of exoskeleton use. However, to use it, we must first understand its characteristics in the context of exoskeleton control. Here, we systematically measured the control preferences of individuals wearing bilateral ankle exoskeletons during walking. We investigated users' repeatability identifying their preferences and how preference changes with walking speed, device exposure, and between individuals with different technical backgrounds. Twelve naive and 12 knowledgeable nondisabled participants identified their preferred assistance in repeated trials by simultaneously self-tuning the magnitude and timing of peak torque. They were blinded to the control parameters and relied solely on their perception of the assistance to guide their tuning. We found that participants' preferences ranged from 7.9 to 19.4 newton-meters and 54.1 to 59.2 percent of the gait cycle. Across trials, participants repeatably identified their preferences with a mean standard deviation of 1.7 newton-meters and 1.5 percent of the gait cycle. Within a trial, participants converged on their preference in 105 seconds. As the experiment progressed, naive users preferred higher torque magnitude. At faster walking speeds, these individuals were more precise at identifying the magnitude of their preferred assistance. Knowledgeable users preferred higher torque than naive users. These results highlight that although preference is a dynamic quantity, individuals can reliably identify their preferences. This work motivates strategies for the control of lower limb exoskeletons in which individuals customize assistance according to their unique preferences and provides meaningful insight into how users interact with exoskeletons.
用户偏好是控制机器人外骨骼的一个很有前途的目标,因为它可能捕捉到外骨骼使用的多因素性质。然而,要使用它,我们必须首先了解它在外骨骼控制中的特点。在这里,我们系统地测量了佩戴双侧踝部外骨骼的个体在行走时的控制偏好。我们研究了用户在重复试验中识别其偏好的可重复性,以及偏好如何随行走速度、设备暴露和具有不同技术背景的个体而变化。12 名新手和 12 名有经验的非残疾参与者通过同时自我调整峰值扭矩的幅度和时间来识别他们的首选辅助。他们对外骨骼的控制参数一无所知,仅依靠对辅助的感知来指导他们的调整。我们发现,参与者的偏好范围从 7.9 到 19.4 牛顿米和 54.1 到 59.2%的步态周期。在试验中,参与者以平均标准差 1.7 牛顿米和 1.5%的步态周期重复地识别出他们的偏好。在一次试验中,参与者在 105 秒内达成一致。随着实验的进行,新手用户更喜欢更高的扭矩幅度。在更快的行走速度下,这些人在识别其偏好的辅助程度上更准确。有经验的用户比新手用户更喜欢更高的扭矩。这些结果表明,尽管偏好是一个动态的数量,但个体可以可靠地识别其偏好。这项工作为控制下肢外骨骼的策略提供了动力,个体可以根据自己的独特偏好定制辅助,为用户与外骨骼的交互提供了有意义的见解。