Markovian Robust Compliance Control Based on Electromyographic Signals
Authors: 
Andres L. Jutinico, Felix M. Escalante, Jonathan C. Jaimes, Marco H. Terra, Adriano A. G. SiqueiraAuthors Info & Claims
Andres L. Jutinico, Felix M. Escalante, Jonathan C. Jaimes, Marco H. Terra, Adriano A. G. SiqueiraAuthors Info & ClaimsPages 1218 - 1223
Abstract
In this paper, we deal with the human-robot interaction control problem. Levels of actuation of the user are considered in the human-robot interaction model from a stochastic point of view. It is given in terms of a Markovian approach. Electromyographic signals are used to compute jump parameters between different levels of interaction. In this way, human neuromuscular system defines the behavior of the Markov chain. A unified approach composed by robust Kalman filter and robust regulator for discrete-time Markovian jump linear systems is proposed. Also, a serious game is used to generate visual feedback and promote the active participation of the user. Experimental results show high accuracy in the Markovian compliance control for a robotic platform applied in ankle rehabilitation.
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Aug 2018
1343 pages
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IEEE Press
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Published: 26 August 2018
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