Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitation robot
Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitation robot
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基于强化学习神经网络（RLNN）的手部精细运动康复机器人自适应控制
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Catherine A. Todd
中科院分区：
文献类型：
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作者：
Xianwei Huang;F. Naghdy;H. Du;G. Naghdy;Catherine A. Todd
最近的神经科学研究表明，机器人设备可以成为提供重复运动训练的有效工具，这种训练是在中风和脊髓损伤（SCI）等神经损伤后触发大脑神经可塑性所需的。在这种情况下，对机器人设备进行自适应控制，以根据需要沿着预期运动轨迹以精确的力强度提供帮助，尽管很复杂，但却是一种更有效的方法。探索基于批评者-行动者的强化学习神经网络（RLNN）控制方法，以在中风后精细手部运动康复训练期间提供自适应控制。通过计算机仿真并在手部康复机器人装置上实现，验证了该方法的有效性。结果表明，该控制系统能够实现高性能、高可靠性的按需辅助（AAN）控制。该方法显示出鼓励患者积极参与康复过程并提高过程效率的潜力。
Recent neural science research suggests that a robotic device can be an effective tool to deliver the repetitive movement training that is needed to trigger neuroplasticity in the brain following neurologic injuries such as stroke and spinal cord injury (SCI). In such scenario, adaptive control of the robotic device to provide assistance as needed along the intended motion trajectory with exact amount of force intensity, though complex, is a more effective approach. A critic-actor based reinforcement learning neural network (RLNN) control method is explored to provide adaptive control during post-stroke fine hand motion rehabilitation training. The effectiveness of the method is verified through computer simulation and implementation on a hand rehabilitation robotic device. Results suggest that the control system can fulfil the assist-as-needed (AAN) control with high performance and reliability. The method demonstrates potential to encourage active participation of the patient in the rehabilitation process and to improve the efficiency of the process.