Hybrid Brain/Muscle Signals Powered Wearable Walking Exoskeleton Enhancing Motor Ability in Climbing Stairs Activity

The powered exoskeleton promises substantial improvements on daily activities of the people who need robots provide assistance. In order to achieve flexible and stable control of a powered lower limb exoskeleton, in this paper, a hybrid control that combines a brain-computer interface (BCI) based on motor imagery (MI) with surface electromyogram (EMG) signals has been developed. We utilized the common spatial pattern (CSP) method to extract the variance of electroencephalogram (EEG) signals and back propagation (BP) neural network to recognize the imagery tasks. Moreover, we have used the strength of EMG signals obtained from upper forearms of subjects to adjust the gait of exoskeleton robots according to real stairs so that subjects can climb stairs easily and stably. The recognized results of EEG and the strength of EMG are used to drive the powered exoskeleton to help subjects climb the stairs by the designed gait synthesis which satisfies the environmental constraint and kinematic constraint. The developed hybrid control strategy has been verified by three healthy subjects, and all subjects can successfully fulfill steadily climbing the stairs, assisted by the powered exoskeleton. The results of the experiment have demonstrated the developed hybrid brain/muscle signals powered robot can effectively enhance human mobility.