Artificial neural network simulation of lower limb joint angles in normal and impaired human gait

Simulating the complexities of lower limb motion can be useful for orthosis or rehabilitation planning. The aim of this study was to develop an artificial neural network (ANN) able to accurately simulate the changes in the angle of the ankle, knee and hip joints during the gait cycle, then to use it to simulate the impact of a restricted range of ankle and hip joint angle changes on the progression of the knee joint angle. Methods: Thirty four young healthy students participated in the study. Gait kinematics data were collected using the Vicon system, then analyzed with an ANN. Results: We developed an ANN able to accurately simulate the progression of joint angles of lower-limb motion; its simulation of the impact of restricted ankle and hip joint angular ranges in the on the knee joint indicate that the braking phase is critical. Conclusions: ANNs offer a useful research method in clinical biomechanics. Further research in this vein should expand our understanding of compensatory functions in the lower limbs.