Predicting triplanar and bidirectional movements for a transtibial prosthesis for rehabilitation using intelligent neural networks

In this study, artificial neural networks (NN) are applied to the design of a transtibial prosthesis to adapt triplanar and bidirectional movements of human locomotion for rehabilitation. NN-based control is used because the prosthesis system is highly nonlinear and has variables with too many uncertainties caused by variations in ankle movements, weight damping, dorsiflexion, and flexion in the amputation area due to biological stimuli. To identify and detect these movements in the transtibial prosthesis, myoelectric signals are used that determine its position and adapt its trajectory through linear and rotary actuators. The input and desired parameters for the NN controller and the backpropagation algorithm are obtained based on the movements of the human ankle and foot based on their trajectory. The prototype is manufactured from different types of plastic using a 3D grapher, which can perform the main stages of human locomotion due to the learning carried out by the NN, reducing the risk of falls, and having a more comfortable and natural gait cycle in the rehabilitation of people. From the output response obtained from the NN controller, only a time delay is obtained without overshoot terms, and the trajectory tracking is adjusted. Simulation and experimental results show that the proposed NN-based control system can ensure the stability of the system and maintain good tracking of human locomotion.