Literature DB >> 31443038

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

Jiajun Xu, Youfu Li, Linsen Xu, Chen Peng, Shouqi Chen, Jinfu Liu, Chanchan Xu, Gaoxin Cheng, Hong Xu, Yang Liu, Jian Chen.   

Abstract

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 AnyBody 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.

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Year:  2019        PMID: 31443038     DOI: 10.1109/TNSRE.2019.2937000

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  2 in total

1.  Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation.

Authors:  Benzhen Guo; Yanli Ma; Jingjing Yang; Zhihui Wang; Xiao Zhang
Journal:  Comput Intell Neurosci       Date:  2020-12-28

2.  Stiffness Analysis of Parallel Cable-Driven Upper Limb Rehabilitation Robot.

Authors:  Yupeng Zou; Xiangshu Wu; Baolong Zhang; Qiang Zhang; Andong Zhang; Tao Qin
Journal:  Micromachines (Basel)       Date:  2022-02-02       Impact factor: 2.891

  2 in total

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