Markov Jump Linear Systems-Based Position Estimation for Lower Limb Exoskeletons

Sensors

Samuel L. Nogueira¹  Adriano A. G. Siqueira¹  Roberto S. Inoue² 

[1] Department of Mechanical Engineering, University of São Paulo, São Carlos, SP 13566-590, Brazil; E-Mail:;Department of Electrical Engineering, Federal University of São Carlos, SP 13565-905, Brazil; E-Mail:

关键词: discrete-time systems;    Markovian jump;    Kalman filter;    ortheses;    exoskeleton;    wearable robots;    lower limbs;   

DOI  :  10.3390/s140101835

来源: mdpi

【 摘 要 】

In this paper, we deal with Markov Jump Linear Systems-based filtering applied to robotic rehabilitation. The angular positions of an impedance-controlled exoskeleton, designed to help stroke and spinal cord injured patients during walking rehabilitation, are estimated. Standard position estimate approaches adopt Kalman filters (KF) to improve the performance of inertial measurement units (IMUs) based on individual link configurations. Consequently, for a multi-body system, like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link position estimation (e.g., the foot). In this paper, we propose a collective modeling of all inertial sensors attached to the exoskeleton, combining them in a Markovian estimation model in order to get the best information from each sensor. In order to demonstrate the effectiveness of our approach, simulation results regarding a set of human footsteps, with four IMUs and three encoders attached to the lower limb exoskeleton, are presented. A comparative study between the Markovian estimation system and the standard one is performed considering a wide range of parametric uncertainties.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.