Neurologically Inspired Transparent Interaction for Wearable Exoskeletons

Abstract

One of the urgent issues with wearable exoskeletons is their difficulty in following human fast movements, especially nonperiodic arm movements. Wearing an exoskeleton can make human movement stiff, which impacts its long-term usability. To mitigate this effect, this article proposes a novel neurologically inspired transparent interaction strategy for arm exoskeletons, which employs a hierarchical controller. First, inspired by the neurological concept of vigor, we propose a top-level predictor to predict arm fast movements in real time. In addition, we incorporate a bidirectional human–robot adaptation strategy into the predictor to enhance the integration of human and exoskeleton. Furthermore, a novel model predictive variable impedance controller is proposed as the mid-level and low-level controllers. We incorporate the interaction force into the weight matrix of the cost function used in model predictive control to further reduce the human–robot interaction force. Experiments using metrics, such as interaction force, surface electromyography, and changes in movement habits, demonstrated that the proposed transparent interaction strategy can effectively reduce the impact of the exoskeleton on users.