A system for augmenting training by Monitoring, Extracting, and Decoding Indicators of Cognitive workload (MEDIC)

US military medical personnel may be deployed to a variety of operational environments where their success saving lives depends on their ability to act quickly and effectively, both as individuals and as teams. Therefore, effective training must go beyond individual skills to include interactions among team members, and how those interactions transfer to operational environments. Currently, trainers must infer competence by observation alonea challenging task. Automatically sensing indicators of cognitive workload can augment performance observations, offering insight into factors underlying that performance. To address this issue, Charles River Analytics has designed and demonstrated the feasibility of a system to augment training by Monitoring, Extracting, and Decoding Indicators of Cognitive workload (MEDIC). Building on that success, we now propose a Phase II effort to iteratively evaluate and refine MEDIC, enabling the disambiguation of potential cognitive workload indicators from other causes, such as physical exertion. Our effort combines: (1) a multimodal suite of unobtrusive, field-ready neurophysiological, physiological, and behavioral sensors; (2) complex event processing to extract and fuse the best indicators of cognitive workload and team dynamics from the multiple, high-volume data streams originating from the sensor suite; and (3) probabilistic modeling techniques to interpret those indicators for easy understanding.