  Simulations are a pedagogical means of enabling a risk-free way for
healthcare practitioners to learn, maintain, or enhance their knowledge and
skills. Such simulations should provide an optimum amount of cognitive load to
the learner and be tailored to their levels of expertise. However, most current
simulations are a one-type-fits-all tool used to train different learners
regardless of their existing skills, expertise, and ability to handle cognitive
load. To address this problem, we propose an end-to-end framework for a trauma
simulation that actively classifies a participant's level of cognitive load and
expertise for the development of a dynamically adaptive simulation. To
facilitate this solution, trauma simulations were developed for the collection
of electrocardiogram (ECG) signals of both novice and expert practitioners. A
multitask deep neural network was developed to utilize this data and classify
high and low cognitive load, as well as expert and novice participants. A
leave-one-subject-out (LOSO) validation was used to evaluate the effectiveness
of our model, achieving an accuracy of 89.4% and 96.6% for classification of
cognitive load and expertise, respectively.
