NDLI: Dual predictive control of electrically stimulated muscle using biofeedback for drop foot correction

Content Provider	IEEE Xplore Digital Library
Author	Hayashibe, Mitsuhiro Zhang, Qin Azevedo-Coste, Christine
Copyright Year	2011
Description	Author affiliation: DEMAR team, INRIA Sophia Antipolis, and LIRMM, UMR 5506 CNRS UM2, 161 Rue Ada, 34095 Montpellier Cedex 5, France (Hayashibe, Mitsuhiro; Zhang, Qin; Azevedo-Coste, Christine)
Abstract	Electrical stimulation (ES) is one of the solutions for drop foot correction. Conventional ES systems deliver predefined stimulation pattern to the affected muscles. However, time-variant muscle response may influence the gait performance as they are difficult to be taken into account in advance. Therefore, closed-loop ES control is important to obtain desired gait in presence of muscle response variation. In this work, a dual predictive control, which consists of two nonlinear generalized predictive controllers, is proposed to track desired torque. The stimulated muscle dynamics are modeled by Hammerstein cascades, with one representing stimulation to activation, the other representing activation to torque. Ankle dorsiflexion torque and ES-evoked EMG of tibialis anterior were recorded experimentally for model identification. The control scheme is validated by following desired torque trajectories with the identified model. The results show that the stimulation pattern obtained from the dual predictive control can produce good torque tracking according to the current muscle condition.
Starting Page	1731
Ending Page	1736
File Size	405485
Page Count	6
File Format	PDF
ISBN	9781612844541
ISSN	21530858
e-ISBN	9781612844565
DOI	10.1109/IROS.2011.6094978
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Torque Muscles Predictive models Predictive control Torque measurement Trajectory Foot
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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