Neural Network Based Modeling and Control of Elbow Joint Motion Under Functional Electrical Stimulation

Publication date: Available online 6 March 2019Source: NeurocomputingAuthor(s): Yurong Li, Wenxin Chen, Jun Chen, Xin Chen, Jie Liang, Min DuAbstractIn 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 el...
Source: Neurocomputing - Category: Neuroscience Source Type: research