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Abstract
It is difficult to objectively measure performance of complex tasks such as a surgical operation and surgical simulators require the ability to evaluate performance whether to predict surgical outcome, determine competence, provide learning feedback, etc. With no standard software framework for collecting, analyzing and evaluating performance data for complex tasks in simulations, it is investigated whether a solution can be implemented that allows for custom data collection schemes, all while being general enough to be used across many simulation platforms and can be used in a simple simulator.It is also investigated whether the implemented framework can perform its functionality while leaving a small performance footprint on the simulator.
Hierarchical task analysis is investigated as a means to decompose complex tasks into their simpler sub-tasks, where data can be collected for each task and evaluated.The framework is based on hierarchical task representation to allow robust performance data of a complex task to be collected and evaluated for any type of application.A client application is developed and allows for the generation of custom scenario parameters for the task, robust performance data collection and the ability to playback previous performances for evaluation purposes.It is shown that the implemented framework has a small peformance footprint and does not affect the performance of the simulator that is using the framework for performance data collection and evaluation.
