The Virtual Assistive Surgical Trainer (VAST) is an approach developed to train surgeons in minimally invasive procedures. It uses surgical instruments augmented with micro-sensors, and knowledge-based inference techniques to provide objective, data-driven feedback and performance assessment for complex exercises. The assessment is typically based on the expertise of senior surgeons and, thus, a single objective standard is difficult to define. To formulate such a standard, and to provide an accurate scoring method, a fuzzy logic method is proposed in this paper. This makes it easier to mimic tasks that are already successfully performed by human experts. A multi-level fuzzy inference engine and new performance metrics are implemented. Experimental results demonstrate the feasibility of this method and the efficacy of the new performance metrics.
['Chuan Feng', 'Jerzy W. Rozenblit', 'Allan J. Hamilton']
Fuzzy Logic-Based Performance Assessment in the Virtual, Assistive Surgical Trainer (VAST)