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Objective Performance Prediction and Optimization using Physiological and Cognitive Metrics
6: Electrical Engineering and Computer Science
Mar 31, 2020
Laura Brattain: firstname.lastname@example.org
Chronic and acute fatigue, task saturation, and neurocognitive overload significantly impair Air Force mission readiness, mission safety, and overall mission success. In fact, fatigue has been identified as a leading cause of Class A mishaps (loss of life and/or loss of aircraft), second only to spatial disorientation. Currently, the objective prediction of performance and personalized optimization of skill acquisition is an open and critical challenge in the training of future Air Force personnel, especially pilots. This Air Force sponsored AI Accelerator project aims to develop and test next-generation AI-based multimodal physiologic sensor fusion approaches for personalized and objective performance prediction and optimization. Centered around realistic, immersive virtual pilot training environments, the project seeks to provide purely unobtrusive performance prediction and develops a series of Challenge Datasets of increasing complexity to engage the community in objective performance prediction based on physiological and neurocognitive data streams. The course of this project is separated into: a) integrating a VR/AR-based flight simulator with a suite of multimodal sensors, b) assisting with human subject data collections including test protocol definition, experimental set-up, and data recording, and c) performing a range of data analysis tasks including processing large signal and image databases, preparing data for machine learning, and implementing data visualization tools. The student will be mentored by MIT Prof Thomas Heldt, and will have the opportunity to work closely with an experienced Air Force pilot, other MIT faculty members on the team, and a number of staff members from MIT Lincoln Laboratory.
Undergraduate student majoring in science or engineering. Proficiency in Matlab or Python. Interests in experimental design and data collection. Experience in flying/piloting is a plus.