Neural network based modeling and control of elbow joint motion under functional electrical stimulation

Abstract

In patients with stroke and spinal cord injury, motor function is reduced or even lost because motor nerve signals cannot be transmitted due to nerve injury. Functional electrical stimulation (FES) is one of the most important rehabilitation techniques for the treatment of motor impairment in patients with stroke and spinal cord injury, which has been widely used in the recovery and reconstruction of limb motor function. In this paper, we propose a neural network based modeling method and control implementation of FES system for upper limb neurorehabilitation. A dynamic neural network model based on Hammerstein structure is proposed for modeling the elbow joint motion under functional electrical stimulation. A closed-loop control system for FES is realized using iterative learning control (ILC) and achieved an excellent tracking performance. Both simulation and experiment are carried out to demonstrate the results. Considering the 20 tests of the model, the average of average relative error (ARE) and root mean square error (RMSE) of the testing samples are 4.11% and 4.12∘, respectively. The ability of ILC system to resist model disturbance is discussed, and the maximum error between the actual elbow joint trajectory and the desired trajectory for each motion cycle is analysed. As the number of iterations increases, the actual elbow motion can track the desired trajectory. The experiment verifies that the real-time system can realize the desired trajectory tracking. The results show that the established dynamic neural network model is suitable for studying the motion characteristics of elbow joint under electrical stimulation. It is feasible to train the network with the aid of genetic algorithm, and the iterative learning strategy can achieve excellent control effect in elbow joint FES system.