This study demonstrated that the physical and cognitive ergonomics with robotic surgery were significantly less challenging. Additionally, several ergonomic components were skill-related. Robotic experts could benefit the most from the ergonomic advantages in robotic surgery. These results emphasize the need for well-structured training and well-defined ergonomics guidelines to maximize the benefits utilizing the robotic surgery.

We apply recurrent neural networks to the task of recognizing surgical activities from robot kinematics. Prior work in this area focuses on recognizing short, low-level activities, or gestures, and has been based on variants of hidden Markov models and conditional random fields. In contrast, we work on recognizing both gestures and longer, higher-level activites, or maneuvers, and we model the mapping from kinematics to gestures/maneuvers with recurrent neural networks. To our knowledge, we are the first to apply recurrent neural networks to this task. Using a single model and a single set of hyperparameters, we match state-of-the-art performance for gesture recognition and advance state-of-the-art performance for maneuver recognition, in terms of both accuracy and edit distance. Code is available at https://github.com/ rdipietro/miccai-2016-surgical-activity-rec.

Our study demonstrated that the current VR simulator offered limited self-skill learning and additional mentoring still played an important role in improving the robotic surgery simulation training.