Intelligent bionic leg motion estimation based on interjoint coordination using PCA and RBF neural networks

It has been a challenging endeavor for amputee to coordinate harmoniously with his/her artificial limb. In this paper, a novel scheme of real-time motion estimation for Intelligent bionic leg based on interjoint coordination is proposed. To measure the gait during walking, inertial sensors are mounted on the CoGs of bilateral thighs and shanks to acquire angular velocities of lower limbs of subjects. For the existence of linear correlation between bilateral kinematics in healthy symmetrical human gait, principle components analysis is employed to model the interjoint coordination and is used to estimate the knee joint angle of intelligent bionic leg from body motion of amputee. To improve the presicion of motion estimation further, RBF neural networks are used to optimally calculate the knee joint angle. Simulation and experimental results demonstrate the effectiveness and correctness of the proposed scheme.