Adaptive Motion Scaling in Teleoperated Robotic Surgery based on Human Intention and Attention

Abstract

In teleoperated surgery, the motion scaling factor directly influences both the operator’s control precision of surgical instruments and operational comfort. Previous studies have revealed that the master manipulator state and operator’s gaze information can reflect the complexity of surgical operations and the operator’s intention to some extent. Although enabling real-time adjustment of scaling factors, they were limited by the narrow range of core parameters and the results were significantly influenced by subjective factors. To tackle these challenges, this paper presents a multi-dimensional adaptive motion scaling strategy based on the Bayesian optimization. The prediction of operator’s intention and attention is achieved by integrating multiple dimensional parameters, including master-slave manipulator states, gaze information, as well as pupillary data, all of which have been experimentally validated. Specifically, there exists a significant temporal synchronization between the Index of Pupillary Activity (IPA) and teleoperation tasks, which aligns with research on the correlation between IPA and attention levels. Furthermore, to evaluate the proposed adaptive scaling strategy, we combine subjective questionnaire surveys with objective metric assessments, effectively reducing the excessive influence of operators’ personal conditions and proficiency levels on optimization results.