Real-time prediction of walking state and percent of gait cycle for robotic prosthetic leg using artificial neural network

This paper describes the walking motion prediction of a user and prediction-based joint trajectory generation of a robotic transfemoral prosthetic leg. To this end, two artificial neural networks are proposed to predict the user’s walking intention and percent of gait cycle (PGC) based on ground foot contact, kinematics, and electromyography (EMG) information. Through the sensor module designed for our study, EMG signals were measured at the tibialis anterior (TA) and gastrocnemius (GAS) of the sound leg, and inertial information was measured at the shin of the sound leg and the thigh of the affected leg. Notably, the time sequence feature data suggested based on the characteristics of the phase portrait of each joint motion showed better learning performance than when using the current time feature data. Walking and standing states were accurately predicted through an artificial neural network (ANN) for walking state classification, and the intention to start and stop walking could be predicted within approximately 83 ms. For generating gait motion of the robotic prosthetic leg, PGC while walking was also accurately predicted through an artificial neural network. Consequently, we developed an algorithm for the generation of gait motion using two artificial neural networks, and its performance was verified through dynamic simulations.