Research on control strategies for ankle rehabilitation using parallel mechanism

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Cognitive Computation and Systems Pub Date : 2020-07-30 DOI:10.1049/ccs.2020.0012
Jianfeng Li, Wenpei Fan, Mingjie Dong, Xi Rong
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引用次数: 14

Abstract

For patients with ankle injuries, rehabilitation training is an important and effective way to help patients restore their ankle complex's motor abilities. Aiming to improve the accuracy and performance of ankle rehabilitation, the authors focus on the control strategies of the developed parallel ankle rehabilitation robot with novel 2-UPS/RRR mechanism. Firstly, the kinematics model of the mechanism is established, and they deduce the inverse solution of positions as well as the velocity mapping between the driving speed and the robot's angular velocity, based on which they realise the trajectory tracking control in the process of passive rehabilitation training. Secondly, they set up experiments to determine the torque threshold that can be used to detect the motion intention of ankle joint, and then they propose the active rehabilitation training strategy according to the motion intention detection. Finally, experiments were carried out with healthy subjects, with results showing that the trajectory tracking error during passive rehabilitation training is very small, and the moving platform of the ankle rehabilitation robot can drive the ankle joint to the detected motion intention direction at a constant speed flexibly and smoothly, which verifies the effectiveness of the control strategies for ankle rehabilitation training.

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并联机构踝关节康复控制策略研究
对于踝关节损伤患者,康复训练是帮助患者恢复踝关节复合体运动能力的重要而有效的方法。为了提高踝关节康复的准确性和性能,作者重点研究了基于新型2-UPS/RRR机构的并联踝关节康复机器人的控制策略。首先,建立机构的运动学模型,推导出机构的位置逆解以及机器人的行驶速度与角速度之间的速度映射关系,并以此为基础实现被动康复训练过程中的轨迹跟踪控制。其次,通过实验确定检测踝关节运动意图的扭矩阈值,并根据检测到的运动意图提出主动康复训练策略。最后,对健康受试者进行了实验,实验结果表明,被动康复训练过程中的轨迹跟踪误差很小,踝关节康复机器人的运动平台能够灵活平稳地匀速驱动踝关节向检测到的运动意图方向运动,验证了踝关节康复训练控制策略的有效性。
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来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
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