Prediction from Expert Demonstrations for Safe Tele-surgery

In this paper, we propose a new prediction from expert demonstration (PED) methodology to improve reliability and safety in tele-surgery. Data was collected from expert (clinician) demonstrations for the procedure of trocar insertion. We encoded a set of force, torque and penetration trajectories by using a Gaussian mixture model (GMM). A generalization of these profiles and associated parameters were retrieved by Gaussian mixture regression (GMR). We validated the proposed methodology for tele-robotic placement of the trocar in two stages. First, we tested the efficacy of the proposed PED approach for handling transmission error and latency. Our results showed that for the average case (12% packet error and 10% loss of packet), a 58.8% improvement in performance was obtained in comparison to using an extended Kalman filter. Next, we validated the methodology for surgical assistance on 15 participants. A haptic assistance mode was devised based on the proposed PED model to assist inexperienced operators to perform the procedure. The PED model was tested for instrument deviation, penetration force and penetration depth. Preliminary study results showed that participants with PED assistance performed the task with more consistency and exerted lesser penetration force than subjects without assistance.