A deep learning strategy for EMG-based joint position prediction in hip exoskeleton assistive robots

Abstract

The exoskeleton robots for the lower limb can help meet the necessary hip joint force to rehabilitate people with movement disorders. This paper proposes a deep learning strategy using electromyography (EMG) signals to predict the human hip joint position and to determine the exoskeleton robot’s necessary auxiliary force at the next step. For this purpose, the EMG signals, force-sensing resistor (FSR), load cell, waist angle with inertial measurement unit (IMU), and hip joint position when walking at speeds of 0.3, 0.4, 0.5, 0.6, and 0.85 m/s for seven healthy individuals are recorded. After the initial preprocessing and extraction of the EMG features, deep neural networks are used in two stages. First, the position of the hip joints is estimated by a convolutional neural network. Second, a long short-term memory (LSTM) network is presented to predict the future hip position. The trained networks are then placed in an impedance control loop, which controls the robot online towards the predicted joint trajectory. Experiments have also been performed to evaluate the accuracy and robustness of the proposed position estimation and prediction algorithm. Results indicate that using the predicted position by the proposed deep strategy reduces the controller error and leads to better synchronization of the exoskeleton robot with its human user, reducing/supporting the required human effort in walking.