Name
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. |
Year | Venue | Field |
|---|---|---|
2018 | BioRob | Regulator,Markov process,Torque,Computer science,Control theory,Markov chain,Robot kinematics,Interaction model,Kalman filter,Jump |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andres L. Jutinico Alarcon | 1 | 0 | 0.34 |
| Felix M. Escalante Ortega | 2 | 0 | 0.34 |
| Jonathan Campo Jaimes | 3 | 3 | 0.76 |
| Marco h. Terra | 4 | 77 | 18.31 |
| Adriano a. g. Siqueira | 5 | 41 | 15.59 |