A Multi-Mode Rehabilitation Robot With Magnetorheological Actuators Based on Human Motion Intention Estimation

被引:49
|
作者
Xu, Jiajun [1 ,2 ]
Li, Youfu [2 ]
Xu, Linsen [3 ,4 ]
Peng, Chen [5 ]
Chen, Shouqi [1 ]
Liu, Jinfu [1 ]
Xu, Chanchan [1 ]
Cheng, Gaoxin [1 ]
Xu, Hong [1 ]
Liu, Yang [6 ]
Chen, Jian [6 ]
机构
[1] Univ Sci & Technol China, Dept Precis Machinery & Instrumentat, Hefei 230026, Anhui, Peoples R China
[2] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China
[3] Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Anhui, Peoples R China
[4] Anhui Key Lab Biomimet Sensing & Adv Robot Techno, Hefei 230031, Anhui, Peoples R China
[5] Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350116, Fujian, Peoples R China
[6] Hefei Univ Technol, Dept Vehicle Engn, Hefei 230009, Anhui, Peoples R China
关键词
Lower extremity rehabilitation robot; MR actuators; human-robot interaction control; EMG; DRIVEN MUSCULOSKELETAL MODEL; IMPEDANCE CONTROL; HYBRID ACTUATOR; MUSCLE; DESIGN; BRAKE; ARM;
D O I
10.1109/TNSRE.2019.2937000
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Lower extremity paralysis has become common in recent years, and robots have been developed to help patients recover from it. This paper presents such a robotic system that allows for two working modes, the robot-active mode and human-active mode. The robot is designed to be equipped with magnetorheological (MR) actuators that have the advantages of high torque, fast response, flexible controllability, low power consumption and safety guarantee. The design and characteristics of the MR actuator are introduced. In the robot-active mode, the MR actuator works as a clutch to transfer the torque to the robotic joint safely. In the human-active mode, the MR actuator functions as a brake to provide resistance to help strengthen muscles. The working mode is determined by the human motion intention, which is detected via the skin surface electromyography (EMG) signals. The human-robot interaction torques are estimated using the EMG-driven impedance model. The biomechanical analysis based on Any Body Modeling System (AMS) is used to help optimization. Then, an adaptive control method is proposed to realize the assist-as-needed (AAN) training strategy, where the robot can switch between these two modes. Experiments are conducted to validate the proposed design.
引用
收藏
页码:2216 / 2228
页数:13
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