Research on control strategies for ankle rehabilitation using parallel mechanism. Issue 3 (30th July 2020)

• Record Type: Journal Article
• Authors: Li, Jianfeng; Fan, Wenpei; Dong, Mingjie; Rong, Xi
• Is Part Of: Cognitive computation and systems. Volume 2:Issue 3(2020)
• Page Start: 105
• Page End: 111
• Publication Date: 2020-07-30
• Subjects: robot kinematics -- injuries -- patient rehabilitation -- medical signal processing -- medical robotics -- position control -- biomechanics; ankle rehabilitation training -- detected motion intention direction -- motion intention detection -- active rehabilitation training strategy -- ankle joint -- passive rehabilitation training -- trajectory tracking control -- developed parallel ankle rehabilitation robot -- ankle complex -- ankle injuries -- parallel mechanism -- control strategies; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs.2020.0012
• Languages: English
• ISSNs: 2517-7567

Abstract

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‐UP S/R RR 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.