Quantifying Performance in Robotic Surgery Training Using Muscle-Based Activity Metrics.

Abstract

Training to perform robotic surgery is time-consuming with uncertain metrics of the level of achieved skill. We tested the feasibility of using muscle co-contraction as a metric to quantify robotic surgical skill in a virtual simulation environment. We recruited six volunteers with varying skill levels in robotic surgery. The volunteers performed virtual tasks using a robotic console while we recorded their muscle activity. A co-contraction metric was then derived from the activity of pairs of opposing hand muscles and compared to the scores assigned by the training software. We found that muscle-based metrics were more sensitive than motion-based scores in quantifying the different levels of skill between simulated tasks and in novices vs. experts across different tasks. Therefore, muscle-based metrics may help quantify in general terms the level of robotic surgical skill and could potentially be used for biofeedback to increase the rate of learning.