Motion marker discovery from inertial body sensors for enhancing objective assessment of robotic surgical skills

Abstract

Robotic surgery simulators are increasingly being used for education in several surgical specialties. However, the surgical skills assessment provided by the robotic simulator is only the statistical evaluation of the stimulus input from the surgeons to the hand and foot actuators, ignoring other body movements and postures that are critical to surgical performance. As a result, performance assessments to surgical experts are not significantly higher than those achieved by novices, and surgical residents do not get the meaningful feedback to improve their skills. In order to enhance the assessment of robotic surgical skills, we employed inertial body sensors and developed a novel motion marker discovery method to determine the differences in motion patterns between expert surgeons and novices on a robotic simulator (Mimic Technologies, Seattle, WA). 4 wireless inertial body sensor nodes capable of 6 degree of freedom sensing were unobtrusively attached to a subject on left wrist, right wrist, right upper arm, and left ankle and used to measure motion during a task. 6 experts and 26 novices participated and performed the Energy Dissection task. Preliminary findings suggested that training with inertial sensor nodes can enhance the robotic surgical training process in a limited amount of time.