A Novel Method for Extracting Surgical Operating Skills Through a Vascular Interventional Surgery Robot

In robot-assisted vascular interventions, the surgeons' operating skills have a significant impact on the outcome of the surgery. However, there is limited research on operation recognition and skill learning of surgeons' operating skills. Most of the studies rely on data collected by multiple external sensors, and the data collection process and recognition algorithms are complex and time consuming, which is not conducive to long term development. In this study, we propose a method for multilevel classification of surgeons' operations and a MultiRocket based classification algorithm. This method can recognize the surgeons' surgical operations while relying only on the data collected by a vascular interventional surgery robot. The results of experiment simulating real surgeries demonstrated the effectiveness of our method with a classification accuracy of 92.56%. This method can qualitative analysis surgeons' operating skills, and can be used in interventionalist training, evaluation of surgical skills, and improvement of the intelligence of robotic systems.