Despite the large number of medical simulators currently available, there is a distinct need for low-cost anatomical training models with integrated sensors capable of recording location and pressure measurements to evaluate a users performance for training in surgical cutting, manipulation and suture. This research effort aims to develop high-fidelity anatomically accurate training models with embedded sensors suitable for evaluating pressure applied to a tissue and the location and depth of a cut as well as to monitor vascular occlusions. Incorporation of embedded sensors into anatomical training models will enable a much higher level of realism in training exercises, and provide quantitative data for a procedure. This data can be compared with a range of acceptable parameters of interest. This means lower cost of training due to less time required from surgeons to look over the students work as well better feedback. It will decrease the amount of time required in the operating room, reduce risk to the patient, lower operational costs, and increase training frequency for students. It also enables customized, patient specific pre-operation models for use in rare or specialized situations that are time critical with potential loss of life.
