CHARM LAB Main/Motor Performance In RAS

Motor Performance In RAS

Project Description

In teleoperated robot-assisted surgery, the patient benefits from the advantages of minimally invasive surgery, such as smaller scars and shorter recovery time, compared to open procedures. In addition, surgeon performance is through to be enhanced by the robotís high dexterity and precision inside the patient's body, as well as improved ergonomics. In 2011, approximately 360,000 clinical procedures were performed with the most popular clinical robot, the da Vinci Surgical System (Intuitive Surgical, Inc.). The popularity of robotic procedures is increasing despite two major problems with existing clinical systems: a steep learning curve and the lack of haptic (touch) feedback to the surgeon.

In this project, by comparing human movements with and without the robot, we are identifying the effects of teleoperation on human performance in robot-assisted surgery, in order to develop scientifically motivated guidelines for robot design and training methods. We are accomplishing this by characterizing the motor control system of surgeons with different levels of expertise (ranging from novices to experts with high volume of surgical cases) when performing tasks under freehand and robotic conditions, with and without changing robotic system properties such as the inertia of the manipulators, motion scaling, and haptic feedback. We are studying simple canonical movements from the study of human motor control, such as reach and reveals movements, as well as clinically relevant movements, such as suture manipulation.

This project will inspire engineering developments that will ultimately improve patient care through better outcomes after surgical procedures, including improved virtual environments for training, robot controllers, and robot designs to increase the usability and accelerate training times for robot-assisted surgical systems. Second, it will provide a unique characterization of human adaptation and skill acquisition that will leverage and extend what is known about human motor control in much simpler environments and tasks.

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