Enhancing Exoskeleton Transparency with Motion Prediction: An Experimental Study

Controlling active exoskeletons for occupational assistance is a challenge. Unlike for rehabilitation exoskeletons, Electromyography (EMG) sensors can hardly be used for control in an industrial environment. The control of assistive exoskeletons needs to rely on onboard sensors to follow the human and assist when needed. This study explores the use of motion prediction, to enhance exoskeleton control in the absence of payloads. When no payloads are involved, the exoskeleton should be transparent meaning that the interaction forces between the exoskeleton and the user should be minimal. We conducted an experiment using a 3D-printed active elbow exoskeleton and compared exoskeleton control methodologies based on dynamic modeling and human motion prediction. Fifteen participants performed a repetitive pointing task under a baseline, two non-predictive controllers and two predictive controllers. The results demonstrated a significant reduction in interaction forces—up to $45 \%$—with predictive controllers compared to non-predictive controllers. While motion prediction enhanced exoskeleton transparency, the force magnitude in this study was small, so users could hardly discern the improvement. Future research will investigate motion prediction for exoskeleton control in the context of load-handling assistance.