Gait modes, such as level walking, stair ascent/descent, and ramp ascent/descent, show different lower-limb kinematic and kinetic characteristics, which are critical for a wearable robot to provide appropriate power assistance. Thus, we develop a fast gait-mode-detection method based on a body sensor system. We use the fuzzy logic algorithm to estimate the likelihoods of gait modes in real time. Since the proposed fast gait mode detection makes it possible to select appropriate kinematic and kinetic models for each gait mode, assistive torques required for assisting the human motions can be obtained more naturally and immediately.
Sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people in daily living activities. We develop a novel impedance modulation strategy of a lower-limb exoskeleton to provide appropriate power and balance assistance during STS movements while preserving the wearer’s control priority. The proposed control strategy ensures adaptation of the mechanical impedance of the human–exoskeleton system toward a desired one requiring less wearer’s effect while reinforcing the wearer’s balance control ability during STS movements. Meanwhile, we design a human joint torque observer to estimate the joint torques developed by the wearer using joint position kinematics, and propose a time-varying desired impedance model according to the wearer’s lower-limb motion ability. The results show that appropriate power assist and balance reinforcement are achieved.
