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For many routine surgical procedures, robot-assisted minimally invasive surgery (RMIS) has become the state of the art, providing surgeons with enhanced dexterity, precision, and control. However, the most effective methods for training surgeons to operate these systems remain unknown, and ineffective training can lead to errors with serious implications for patient safety. In this talk, I will present my research into optimizing training for surgical robotics, drawing on user studies involving the design, manipulation, and evaluation of multisensory performance feedback. I will also discuss ongoing work on integrating biomechanical performance metrics into the training process to further enhance skill acquisition and retention.
Mary Kate Gale (Stanford University)
Mary Kate Gale is a fourth year Ph.D. candidate in Bioengineering at Stanford University. She earned her B.S. in Biomedical Engineering at Georgia Institute of Technology. At Stanford, she works with Prof. Allison Okamura in the Collaborative Haptics and Robotics in Medicine (CHARM) Lab, which focuses on the design and application of haptic and soft robotic technologies. Her research focuses on human motor learning neuroscience through the lens of teleoperation of surgical robots.
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