PROJECT TITLE :

Perceptually Augmented Simulator Design

ABSTRACT:

Training simulators have proven their worth in a variety of fields, from piloting to air-traffic control to nuclear power station monitoring. Designing surgical simulators, however, poses the challenge of creating trainers that effectively instill not only high-level understanding of the steps to be taken in a given situation, but also the low-level “muscle-memory” needed to perform delicate surgical procedures. It is often impossible to build an ideal simulator that perfectly mimics the haptic experience of a surgical procedure, but by focussing on the aspects of the experience that are perceptually salient we can build simulators that effectively instill learning. We propose a general method for the design of surgical simulators that augment the perceptually salient aspects of an interaction. Using this method, we can increase skill-transfer rates without requiring expensive improvements in the capability of the rendering hardware or the computational complexity of the simulation. In this paper, we present our decomposition-based method for surgical simulator design, and describe a user-study comparing the training effectiveness of a haptic-search-task simulator designed using our method versus an unaugmented simulator. The results show that perception-based task decomposition can be used to improve the design of surgical simulators that effectively impart skill by targeting perceptually significant aspects of the interaction.