Prediction of lower extremities movement using characteristics of angle-angle diagrams and artificial intelligence

Human gait is nowadays undergoing extensive analysis. Our work focuses on predicting human gait with the use of angle-angle diagrams, also called cyclograms. In conjunction with artificial intelligence, cyclograms offer a wide area of medical applications. Predictions of leg movements can be used for orthosis and prosthesis programming, and also for rehabilitation. We have identified cyclogram characteristics such as the slope and the area of the cyclogram for a neural network learning algorithm. Neural networks learned by cyclogram characteristics predicted cyclogram curve and offer wide applications in prosthesis control systems.