Fuzzy Logic-Based Performance Assessment in the Virtual, Assistive Surgical Trainer (VAST)

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.