Adaptive control with state-dependent modeling of patient impairment for robotic movement therapy.
Adaptive control with state-dependent modeling of patient impairment for robotic movement therapy.
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DOI：
10.1109/icorr.2013.6650460
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发表时间：
2013-06
期刊：
影响因子：
--
通讯作者：
Wolbrecht E
中科院分区：
文献类型：
--
作者：
Bower C;Taheri H;Wolbrecht E
本文提出了一种自适应控制方法，机器人运动治疗，学习状态依赖模型的患者损伤。与以前的工作不同，这种方法使用非结构化的惯性模型，依赖于机器人的工作空间中所需的运动的位置和方向。该方法学习患者损伤模型，该模型解释了神经肌肉输出中的运动特定残疾（例如屈曲与伸展以及缓慢与动态任务）。与按需辅助力衰减相结合，这种方法可以进一步促进患者的参与和参与。使用机器人治疗设备，手指（手指个性化抓取运动机器人），几个实验，以证明自适应控制的能力，学习状态依赖的能力。
This paper presents an adaptive control approach for robotic movement therapy that learns a state-dependent model of patient impairment. Unlike previous work, this approach uses an unstructured inertial model that depends on both the position and direction of the desired motion in the robot’s workspace. This method learns a patient impairment model that accounts for movement specific disability in neuromuscular output (such as flexion vs. extension and slow vs. dynamic tasks). Combined with assist-as-needed force decay, this approach may promote further patient engagement and participation. Using the robotic therapy device, FINGER (Finger Individuating Grasp Exercise Robot), several experiments are presented to demonstrate the ability of the adaptive control to learn state-dependent abilities.