A Fuzzy Fusion Approach for the Prediction of Position and Orientation in Robot-Assisted Cardiovascular Surgery

To assist doctors in cardiovascular intervention, the use of robots in surgery can effectively separate interventionalists from X-ray radiation, prevent the spread of respiratory diseases, perform remote operations to alleviate the regional imbalance of medical resources and address some procedural challenges. However, a lack of force feedback in human computer interaction of current surgical robots affects surgical efficiency and safety. In cardiovascular surgery, the catheter bends and twists as it is the dynamic multipoint line contact with the vessel. It is difficult to accurately model and register it. In addition, time delay affects system transparency. This paper proposes a multi-information fuzzy fusion approach that is based on medical experience in order to predict position and orientation online. More specifically, Fitts law is developed with a two-motion collaborative to estimate the surgical movement time. Experimental results reveal that the proposed method can result in high system transparency in robot-assisted cardiovascular surgery. It could further improve accuracy and security in surgical procedures.