A Smart Mobile Robotic Exoskeleton by Man-Machine Interaction Signal Processing

Abstract

A new mobile robotic hip exoskeleton has been presented to meet the early rehabilitation needs of patients with neurologically dysfunction, which can be controlled by the active motion intention of patients. This lower limb rehabilitation robot is composed of two exoskeleton orthotics and a robotic walker, providing an over-ground walking experience and an active torque to unload body weight. Several sensors are placed on the robot to detect motion intention and ensure safety. An autonomous control strategy is proposed to enhance user's active participation during gait rehabilitation. Kalman filter is used to process the human-machine interaction force signal, and the trajectory equation is obtained by fitting the referenced hip joint curve which extracted from the clinical gait analysis database. The result of designed experiments is in line with the expected performance, tracking error between the reference trajectory and the actual trajectory is about 1 deg.