步态康复中三元人-机器人-FES协作的自适应控制：一项初步研究
Adaptive Control for Triadic Human-Robot-FES Collaboration in Gait Rehabilitation: A Pilot Study
AI总结：
本文提出一种自适应混合机器人-FES控制器，通过患者驱动与分层辅助实现三元协作，在仿真和健康受试者测试中提升了跟踪性能、降低辅助强度并减少肌肉疲劳。
摘要：
机器人辅助步态训练与功能性电刺激（FES）的混合化能够为神经疾病患者提供诸多生理益处。然而，设计一种有效的混合控制器面临重大挑战。在这个过驱动系统中，极难在机器人辅助和FES之间找到合适的平衡，以提供个性化辅助、防止肌肉疲劳并鼓励患者主动参与，从而加速康复。本文提出了一种自适应混合机器人-FES控制器来实现这一目标，并实现患者、机器人和FES之间的三元协作。设计了一种患者驱动控制器，优先考虑患者的自主运动，并根据患者的表现及其肌肉健康状况，按分层顺序使用FES和机器人提供辅助。该混合自适应控制器的性能在仿真和一名健康受试者身上进行了测试。结果表明，与非自适应等效控制器相比，使用混合自适应控制器时，跟踪性能提高且总体辅助降低，肌肉疲劳减少。这表明我们的混合自适应控制器能够适应用户行为，按需提供辅助，并防止因肌肉疲劳导致物理治疗的提前终止。
英文摘要：
The hybridisation of robot-assisted gait training and functional electrical stimulation (FES) can provide numerous physiological benefits to neurological patients. However, the design of an effective hybrid controller poses significant challenges. In this over-actuated system, it is extremely difficult to find the right balance between robotic assistance and FES that will provide personalised assistance, prevent muscle fatigue and encourage the patient's active participation in order to accelerate recovery. In this paper, we present an adaptive hybrid robot-FES controller to do this and enable the triadic collaboration between the patient, the robot and FES. A patient-driven controller is designed where the voluntary movement of the patient is prioritised and assistance is provided using FES and the robot in a hierarchical order depending on the patient's performance and their muscles' fitness. The performance of this hybrid adaptive controller is tested in simulation and on one healthy subject. Our results indicate an increase in tracking performance with lower overall assistance, and less muscle fatigue when the hybrid adaptive controller is used, compared to its non adaptive equivalent. This suggests that our hybrid adaptive controller may be able to adapt to the behaviour of the user to provide assistance as needed and prevent the early termination of physical therapy due to muscle fatigue.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
