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<italic>iLeg</italic>—A Lower Limb Rehabilitation Robot: A Proof of Concept
64
Citations
23
References
2016
Year
Gait AnalysisEngineeringNeural ControlActive TrainingMotor ControlMovement AnalysisRehabilitation RoboticsKinesiologyKinematicsRehabilitation EngineeringProsthesisHealth SciencesPassive Training ControllerAssistive TechnologyMechatronicsRehabilitationPerceptron Neural NetworkPhysical TherapyBipedal LocomotionMotion ControlElectromyographyHuman MovementRobotics
In this paper, a robot, namely iLeg, is designed for the purpose of rehabilitation of patients with hemiplegia or paraplegia. The iLeg is composed of one reclining seat and two leg orthoses, and each leg orthosis has three degrees of freedom, which correspond to the hip, knee, and ankle. Based on this robotic system, two controllers, i.e., passive training controller and active training controller, are proposed. The former takes advantage of the proportional-integral control method to solve the trajectory tracking problem, and the latter employs the surface electromyography signals to achieve active training. Two simplified impedance controllers, i.e., damping-type velocity controller and spring-type position controller, are designed for active training. A perceptron neural network detects movement intentions. The performance of the controllers was investigated with one able-bodied male. The results showed that the leg orthosis tracked the predefined trajectory based on the passive training controller, with the error rates of 0.45%, 0.44%, and 0.27%, respectively, for the hip, knee, and ankle. The active training controller whose loop rate is 6.67 Hz can move the leg orthosis smoothly, and the average recognition error of the perceptron neural network is less than 5%.
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