EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot

控制器(灌溉) 物理医学与康复 导纳 康复 机器人 计算机科学 弹道 肌电图 人工神经网络 外骨骼 模拟 刚度 人工智能 物理疗法 工程类 医学 结构工程 生物 电气工程 物理 电阻抗 农学 天文
作者
Maryam Mashayekhi,Majid M. Moghaddam
出处
期刊:Journal of Biomechanics [Elsevier BV]
卷期号:138: 111104-111104 被引量:9
标识
DOI:10.1016/j.jbiomech.2022.111104
摘要

Upper-limb rehabilitation therapy sessions for post-stroke people generally contain rhythmic hand movements in a tiresome manner to rebuild the injured neural circuits. Fatigue formation causes breaks in the training and limits the therapy duration. Therefore, it is essential to establish a correlation between the patient's muscle condition and the rehabilitation exercises to improve the physiotherapy sessions. A self-adapting control method based on online fatigue detection in rhythmic arm movements is presented. Experimental tests were performed on twenty healthy subjects to validate the method's feasibility. Electromyography (EMG) and force signals considered the interfaces between users and the robot. In the first stage of the experiment, utilizing the frequency features from EMG signals, a neural network for fatigue detection trained; however, in the end, it substituted with a simple function as a refinement in the time-consuming aspect for the online employment. The initiation of the fatigue process is followed by reducing the admittance controller damping term based on the EMG signal processing. Trajectory tracking with the robot employs the self-adapting admittance controller (SAAC) method and the non-adapting admittance controller (NAAC). Movement accuracy and smoothness were measured and showed a better performance of the SAAC method related to the NAAC. Simulations with two different stiffness levels were performed on an upper-limb OpenSim model to study a stroke-injured arm and evaluate the proposed method's proficiency. The metabolic cost indicated the movement's superiority in a fatigue situation for reduced environment stiffness.
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