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

Award Information
Agency:
Department of Defense
Branch:
Defense Health Program
Amount:
$150,000.00
Award Year:
2014
Program:
SBIR
Phase:
Phase I
Contract:
W81XWH-14-C-0018
Agency Tracking Number:
H132-002-0021
Solicitation Year:
2013
Solicitation Topic Code:
DHP13-002
Solicitation Number:
2013.2
Small Business Information
Charles River Analytics Inc.
625 Mount Auburn Street, Cambridge, MA, 02138-
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
Y
Duns:
115243701
Principal Investigator
 Bethany Bracken
 Scientist
 (617) 491-3474
 bbracken@cra.com
Business Contact
 Mark Felix
Title: Contracts Manager
Phone: (617) 491-3474
Email: mfelix@cra.com
Research Institution
N/A
Abstract
Military medical personnel must act quickly and efficiently in any operational environment. Their success in saving lives depends on their ability to act effectively, both individually and as a team. Therefore, training must address individual skills and knowledge as well as interactions among team members. Currently, trainers must infer competence across these dimensions using only observation of trainee actiona challenging task. Automatically sensing indicators of cognitive load can provide information that augments performance observations, offering insight into how individuals and teams achieved that performance. Therefore, we propose to design and demonstrate a system for augmenting training by Monitoring, Extracting, and Decoding Indicators of Cognitive Load (MEDIC). MEDIC will use a multimodal suite of unobtrusive, field-ready neurophysiological and physiological sensors to disambiguate potential cognitive load indicators from other causes, such as physical exertion. MEDIC's sensor suite includes a user interface for trainers to enter observations and annotations for later review. MEDIC will use 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. MEDIC will also use novel probabilistic modeling techniques to help trainers interpret indicators during and after training.

* information listed above is at the time of submission.

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