NDLI: FES-induced muscular torque prediction with evoked EMG synthesized by NARX-type recurrent neural network

Content Provider	IEEE Xplore Digital Library
Author	Zhan Li Hayashibe, M. Qin Zhang Guiraud, D.
Copyright Year	2012
Description	Author affiliation: Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology under JSPS postdoc program, Japan (Qin Zhang) \|\| INRIA Sophia-Antipolis DEMAR team, and LIRMM CNRS/University of Montpellier, UMR 5506, 95 Rue de la Galéra, 34095 cedex 5, France (Zhan Li; Hayashibe, M.; Guiraud, D.)
Abstract	Functional electrical stimulation (FES) is able to restore motor function of spinal cord injured (SCI) patients. To make adaptive FES control taking into account the actual muscle state with muscular feedback information, torque estimation and prediction are important to be provided beforehand. Evoked EMG (eEMG) has been found to be highly correlated with FES-induced torque under various muscle conditions, indicating that it can be an useful tool for torque/force prediction. To better construct the relationship between eEMG and stimulated muscular torque, nonlinear-arx-type (NARX-type) model is preferred. This paper presents and exploits a NARX-type recurrent neural network (NARX-RNN) model for identification and prediction of FES-induced muscular dynamics with eEMG. Such NARX-RNN model is with a novel architecture for prediction, with robust prediction performance. To make fast convergence for identification of such NARX-RNN, directly-learning pattern is exploited during the learning phase. Due to difficulty of choosing a proper forgetting factor of Kalman filter for predicting time-variant torque with eEMG, such NARX-RNN may be considered to be a better alternative as torque predictor. Data gathered from two SCI patients is used to evaluate the proposed NARX-RNN model. The NARX-RNN model shows promising estimation and prediction performance only based on eEMG.
Starting Page	2198
Ending Page	2203
File Size	925144
Page Count	6
File Format	PDF
ISBN	9781467317375
ISSN	21530858
e-ISBN	9781467317368
DOI	10.1109/IROS.2012.6385602
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-07
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Torque Muscles Predictive models Kalman filters Torque measurement Time measurement Estimation
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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