Development of a System for Real-Time Assessment and Modification of Team-Based Exercises

This project seeks to develop a Multimodal Performance Evaluation System (MPES), a system for real-time evaluation of individual and team stress levels suitable for a wide array of military medical training and assessment domains and conditions. This system was designed to identify changes in stress levels during training scenarios and determine whether changes to environmental or task stressors are needed. MPES capitalizes on the predictive abilities of pretest data (cognitive and affective) along with real-time physiological and sociometric data, to make claims about individual team member stress levels during training and aggregating that data into a team stress-level score. Our overarching goal was to improve the quality of training for medical teams, by adapting scenario-related characteristics to maximize stress levels, while avoiding overstressing to the point of performance failure. MPES is designed to be utilized in a variety of training environments of varying complexity, including screen-based, headmount, immersive VR theatre, and open field exercises. Independent variables include heart rate, skin temperature, vocal characteristics. Moderating variables are working memory capacity and eight affective variables (e.g., extroversion and emotional stability). A Machine Learning approach using TPOT (a Python Automated Machine Learning Tool) was utilized for validation of the MPES analytic engine. Results indicate that the system is sensitive to individual differences and is capable of reliably reporting on perceived stress level.