Markov Jump Linear Systems-based position estimation for lower limbs exoskeletons

This paper deals with Markov Jump Linear Systems-based filtering applied to robotic rehabilitation. 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 have adopted Kalman Filters (KF) to improve measurement quality of inertial sensors based on individual link configurations. That is, 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 it is proposed a collective modeling of all inertial sensors attached to the device, combining them in a Markovian estimation model, in order to get the best information from each sensor. To demonstrate the efficiency of our approach, a simulation was performed regarding a set of human footsteps, with four IMUs and three encoders attached to the lower limb exoskeleton. A comparative study between the Markovian estimation system and the standard one is performed considering a wide range of parametric uncertainties.