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A New Single-Leg Lower-Limb Rehabilitation Robot: Design, Analysis and Experimental Evaluation

Hongfei Yu, Siyuan Zheng, Jiantao Wu, Li Sun, Yongliang Chen, Zhongzhi Qin

Year
2023
Citations
11
Access
Open access

Abstract

Conventional lower-limb rehabilitation robots are large, complicated to wear, and require moving the patient to a designated position. To solve these problems, a new single-legged lower-limb rehabilitation robot (S-LLRR) that is easy to move and suitable for different height carriers was proposed. The mechanical leg has a variable working space, and its rotating joints are designed with mechanical limiters. The series configuration of the S-LLRR was analyzed, and trajectory planning was performed based on continuous linear motion training. Meanwhile, an active training control method based on the sand model was proposed to enhance the motion sensation of patients, and an active participation degree evaluation model was designed based on human physiological information. The simulation and experimental results showed that S-LLRR had a large workspace and good motion accuracy, and the accuracy of the active participation degree evaluation model could reach more than 85%. This research could provide a theoretical basis for improving the standardization and compliance of lower-limb robot rehabilitation training.

Keywords

WorkspaceRehabilitationSimulationComputer scienceTrajectoryRobotMotion (physics)Lower limbPhysical medicine and rehabilitationArtificial intelligence

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