Computational Intelligence - May 2014 - 38
Muscle Fatigue
Tracking with
Evoked EMG via
Recurrent Neural
Network: Toward
Personalized
Neuroprosthetics
Abstract-One of the challenging issues in computational rehabilitation is
that there is a large variety of patient situations depending on the type of
neurological disorder. Human characteristics are basically subject specific and
time variant; for instance, neuromuscular dynamics may vary due to muscle
fatigue. To tackle such patient specificity and time-varying characteristics,
a robust bio-signal processing and a precise model-based control
which can manage the nonlinearity and time variance of the system, would bring break-through and new modality toward comZhan Li and Mitsuhiro Hayashibe
putational intelligence (CI) based rehabilitation technology and
INRIA Demar team and LIRMM,
personalized neuroprosthetics. Functional electrical stimulaCNRS/University of Montpellier II,
tion (FES) is a useful technique to assist restoring motor
Montpellier, FRANCe
capability of spinal cord injured (SCI) patients by delivering
electrical pulses to paralyzed muscles. However, muscle
Charles Fattal
fatigue constraints the application of FES as it results in the
time-variant muscle response. To perform adaptive closedCentre Mutualiste Neurologique PROPARA,
loop FES control with actual muscle response feedback
Montpellier, FRANCe
taken into account, muscular torque is essential to be estimated accurately. However, inadequacy of the implantable
David Guiraud
torque sensor limits the direct measurement of the time-variINRIA Demar team and LIRMM,
ant torque at the joint. This motivates the development of
CNRS/University of Montpellier II,
methods to estimate muscle torque from bio-signals that can be
Montpellier, FRANCe
measured. Evoked electromyogram (eEMG) has been found to be
highly correlated with FES-induced torque under various muscle
conditions, indicating that it can be used for torque/force prediction. A
nonlinear ARX (NARX) type model is preferred to track the relationship
between eEMG and stimulated muscular torque. This paper presents a
Image lIcensed by Ingram PublIshIng
This work was supported by ANR SoHuSim and FP7 European TIME projects.
Digital Object Identifier 10.1109/MCI.2014.2307224
Date of publication: 11 April 2014
38
IEEE ComputatIonal IntEllIgEnCE magazInE | may 2014
1556-603x/14/$31.00©2014IEEE
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