Towards Objective Surgical Skill Evaluation with Hidden Markov Model-based Motion Recognition
نویسندگان
چکیده
Modern surgical trainees are often given written examinations to test their knowledge and decision-making skills. However, there exists no widely accepted method for objective evaluation of technical skill. The need for such a method is particularly evident in the young field of robot-assisted minimally invasive surgery, where specific training methods have not been fully established and little is known about surgeons’ skill acquisition. Our approach to objective evaluation is based on the assumption that technical skill will reveal itself in the motions used to complete a surgical task. We collect detailed motion data from Intuitive Surgical’s daVinci robotic surgical system during the performance of such tasks and automatically segment and recognize these motions. With a list of the motions used to complete a task, we may evaluate skill by comparing the number, distribution, and sequences of motions used by novices and experts. Our methodology is comprised of four major steps. First, a motion vocabulary must be defined. Second, segmenting the data into individual motions is done using the Cartesian velocities of surgeon’s input motions. Third, individual motions are automatically recognized using hidden Markov models; recognition rates have been
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