Abstract
Integration of robotic systems in the operating room changed the way that surgeries are performed. It modifies practices to improve medical benefits for the patient but also brought non-traditional aspects that can lead to serious undesirable effects. Recent studies from the French authorities for hygiene and medical care highlight that these undesirable effects mainly come from the surgeon's technical skills, which question surgical robotic training and teaching. To overcome this issue, surgical simulators help to train practitioner through different training tasks and provide feedback to the operator. However the feedback is partial and do not help the surgeon to understand gestural mistakes. Thus, we want to improve the surgical robotic training conditions. The objective of this work is twofold. First, we developed a new method for segmentation and recognition of surgical gestures during training sessions based on an unsupervised approach. From surgical tools kinematic data, we are able to achieve gesture recognition at 82%. This method is a first step to evaluate technical skills based on gestures and not on the global execution of the task as it is done nowadays. The second objective is to provide easier access to surgical training and make it cheaper. To do so, we studied a new contactless human-machine interface to control surgical robots. In this work, the interface is plugged to a Raven-II robot dedicated to surgical robotics research. Then, we evaluated performance of such system through multiple studies, concluding that this interface can be used to control surgical robots. In the end, one can consider to use this contactless interface for surgical training with a simulator. It can reduce the training cost and also improve the access for novice surgeons to technical skills training dedicated to surgical robotics.