An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing-Demonstration of a Semi-autonomous Suction Tool.

Abstract

This study developed and evaluated a mental workload-based adaptive automation (MWL-AA) that monitors surgeon cognitive load and assist during cognitively demanding tasks and assists surgeons in robotic-assisted surgery (RAS). The introduction of RAS makes operators overwhelmed. The need for precise, continuous assessment of human mental workload (MWL) states is important to identify when the interventions should be delivered to moderate operators' MWL. The MWL-AA presented in this study was a semi-autonomous suction tool. The first experiment recruited ten participants to perform surgical tasks under different MWL levels. The physiological responses were captured and used to develop a real-time multi-sensing model for MWL detection. The second experiment evaluated the effectiveness of the MWL-AA, where nine brand-new surgical trainees performed the surgical task with and without the MWL-AA. Mixed effect models were used to compare task performance, objective- and subjective-measured MWL. The proposed system predicted high MWL hemorrhage conditions with an accuracy of 77.9%. For the MWL-AA evaluation, the surgeons' gaze behaviors and brain activities suggested lower perceived MWL with MWL-AA than without. This was further supported by lower self-reported MWL and better task performance in the task condition with MWL-AA. A MWL-AA systems can reduce surgeons' workload and improve performance in a high-stress hemorrhaging scenario. Findings highlight the potential of utilizing MWL-AA to enhance the collaboration between the autonomous system and surgeons. Developing a robust and personalized MWL-AA is the first step that can be used do develop additional use cases in future studies. The proposed framework can be expanded and applied to more complex environments to improve human-robot collaboration.