Towards integrating task information in skills assessment for dexterous tasks in surgery and simulation

Abstract

With the increasing popularity of robotic surgery, several studies in the literature have investigated automatically assessing skill measures based on motion and video data captured from these systems. A range of simulation environments for robotic surgery are now in development. Skill assessment in these environments has so far only focused on evaluating the utility and validity of statistics such as task completion time, and instrument distance measured during a simulated task. We present the first work using motion data from a robotic surgery simulation environment in development for classifying users of varying skills and detecting completion of trainee. Given the standardized environment of the simulator, and the availability of the ground truth, skill measurements and feedback based on task motion hold the promise of effective automated objective assessment. Based on motion data of a simulated manipulation task from 17 users of varying skills, we demonstrate binary classification (proficient vs. trainee) of user skill with 87.5% accuracy. Alternate measures based on instrument pose more relevant in the simulated environment including a new measure of motion efficiency are also presented and evaluated.