Paper Info
Title
Estimating The Ankle Angle Induced By Fes Via The Neural Network-Based Hammerstein Model
Abstract
Functional electrical stimulation (FES) has been widely used in limb rehabilitation. The first step for the precision rehabilition is to clarify the variation of limb angle induced by FES. In this study, an electric stimulator and an inertial sensor are used to build a human body experimental platform. Motion characteristics of ankle angle induced by electrical stimulation pulse variation are obtained through experiment. The obtained ankle angle characteristics are used to train a neural network-based Hammerstein (H) model and the model parameters are identified by the genetic algorithm, which can effectively predict the ankle angle change induced by electrical stimulation. The structural parameters of the H model are adjusted according to the normalized root mean square error value (NRMSE) of the training data. The 10-fold cross-validation is used to verify the feasibility and effectiveness of the model. Experimental results show that the neural network-based H model can effectively predict the output change of the ankle angle induced by the electrical stimulation pulse, and its root mean square error (RMSE) and NRMSE are 2.78 +/- 0.33 degrees and 23.70 +/- 1.77%, respectively. Therefore, the proposed model can provide a theoretical basis for predicting ankle angle change in an electrical stimulation closed-loop control system.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2943453
IEEE ACCESS
Keywords
DocType
Volume
Electrical stimulation, Muscles, Iron, Computational modeling, Biological neural networks, Data models, Functional electrical stimulation, ankle angle, Hammerstein model, neural network, genetic algorithm
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yihua Zhou175.33
L. K. Huang200.34
Yue-Ming Gao3188.88
Zeljka Lucev Vasic423.68
Mario Cifrek52612.64
Min Du65011.13