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http://dx.doi.org/10.5302/J.ICROS.2011.17.10.1021

Optimal Design of a Novel Knee Orthosis using a Genetic Algorism  

Pyo, Sang-Hun (Gyeongsang National University)
Yoon, Jung-Won (Gyeongsang National University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.17, no.10, 2011 , pp. 1021-1028 More about this Journal
Abstract
The objective of this paper is to optimize the design parameters of a novel mechanism for a robotic knee orthosis. The feature of the proposed knee othosis is to drive a knee joint with independent actuation during swing and stance phases, which can allow an actuator with fast rotation to control swing motions and an actuator with high torque to control stance motions, respectively. The quadriceps device operates in five-bar links with 2-DOF motions during swing phase and is changed to six-bar links during stance phase by the contact motion to the patella device. The hamstring device operates in a slider-crank mechanism for entire gait cycle. The suggested kinematic model will allow a robotic knee orthosis to use compact and light actuators with full support during walking. However, the proposed orthosis must use additional linkages than a simple four-bar mechanism. To maximize the benefit of reducing the actuators power by using the developed kinematic design, it is necessary to minimize total weight of the device, while keeping necessary actuator performances of torques and angular velocities for support. In this paper, we use a SGA (Simple Genetic Algorithm) to minimize sum of total link lengths and motor power by reducing the weight of the novel knee orthosis. To find feasible parameters, kinematic constraints of the hamstring and quadriceps mechanisms have been applied to the algorithm. The proposed optimization scheme could reduce sum of total link lengths to half of the initial value. The proposed optimization scheme can be applied to reduce total weight of general multi-linkages while keeping necessary actuator specifications.
Keywords
knee orthosis; biomechanics; gait rehabilitation; exoskeleton; genetic algorithm; weight optimization;
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Times Cited By KSCI : 2  (Citation Analysis)
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