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Velocity Field based Active-Assistive Control for Upper Limb Rehabilitation Exoskeleton Robot

机译:基于速度场的上肢康复外骨骼机器人主动控制

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There are limitations of conventional active-assistive control for upper limb rehabilitation exoskeleton robot, such as 1). prior time-dependent trajectories are generally required, 2). task-based rehabilitation exercise involving multi-joint motion is hard to implement, and 3). assistive mechanism normally is so inflexible that the resulting exercise performed by the subjects becomes inefficient. In this paper, we propose a novel velocity field based active-assistive control system to address these issues. First, we design a Kalman filter based interactive torque observer to obtain subjects’ active intention of motion. Next, a joint-position-dependent velocity field which can be automatically generated via the task motion pattern is proposed to provide the time-independent assistance to the subjects. We further propose a novel integration method that combines the active and assistive motions based on the performance and the involvement of subjects to guide them to perform the task more voluntarily and precisely. The experiment results show that both the execution time and the subjects’ torque exertion are reduced while performing both given single joint tasks and task-oriented multi-joint tasks as compared with the related work in the literature. To sum up, the proposed system not only can efficiently retain subjects’ active intention but also can assist them to accomplish the rehabilitation task more precisely.
机译:对于上肢康复外骨骼机器人,传统的主动辅助控制存在局限性,例如1)。通常需要事先依赖于时间的轨迹; 2)。涉及多关节运动的基于任务的康复锻炼很难实施; 3)。辅助机制通常过于僵化,以至于受试者进行的运动效率低下。在本文中,我们提出了一种新颖的基于速度场的主动-主动控制系统来解决这些问题。首先,我们设计了一个基于卡尔曼滤波器的交互式扭矩观察器,以获取受试者的主动运动意图。接下来,提出了一种可以通过任务运动模式自动生成的关节位置相关的速度场,为受试者提供与时间无关的帮助。我们进一步提出了一种新颖的整合方法,该方法基于主体的表现和参与程度将主动和辅助运动结合起来,以指导他们更自愿,更准确地执行任务。实验结果表明,与文献中的相关工作相比,既执行单关节任务又执行面向任务的多关节任务时,执行时间和对象的扭矩施加都减少了。综上所述,所提出的系统不仅可以有效地保持受试者的积极意图,而且可以帮助他们更精确地完成康复任务。

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