We developed a robotic lower-limb exoskeleton for those who have weakened muscle due to aging and experience difficulty in walking or getting up without help. The exoskeleton covering both limbs from the feet to the waist has 6 electric actuators in the hip abduction/adduction, hip extension/flexion and knee extension/flexion joints. For users with volitional motion, delivering assistance power according to their intention is a challenging task. We propose an adaptive oscillator-based controller to assist users walk in the lower-limb exoskeleton. To adapt to changes in walking speed and environment, motion command from the controller is modulated by estimate walking speed and walking environment recognized as one of the following categories: level ground, stairs up/down and slope up/down. Experimental results demonstrate the feasibility of the proposed environment recognition method and the impact of assistance on the metabolic cost of walking on level and inclined treadmills.

We propose a novel two-tier gait recognizer using a minimal number of mechanical sensors built into a lower limb exoskeleton. The aim of this recognizer is to offer one-step selection of one of the actions ascending, descending, and level walking during five gaits (stair ascent/descent, slope ascent/descent, level walking). The proposed recognizer is executed at the moment of foot contact as estimated by an inertial measurement unit (IMU) on the pelvis without using direct foot sensors. The proposed recognizer selects the gait by using the relations between the angles formed by the hip and knee joints during the last step. As this study constitutes preliminary work for lower limb exoskeletons, we used a hip exoskeleton integrated with two wireless IMUs on the shank to set up the lower limb exoskeleton sensor configuration. Experiments were used to evaluate IMU-based foot contact estimation with respect to a foot sensor based on a force-sensitive resistor (FSR) for stairs, slopes, and level ground. In addition, we evaluated the performance of the proposed two-tier gait recognizer by using two healthy male subjects in various gait environments.