Charles Rivers Analytics wins US Army contract to field-test MEDIC

Charles Rivers Analytics wins US Army contract to field-test and refine MEDIC

Charles River Analytics won a two-year, $1 million U.S. Army contract to field-test, refine and transition its Monitoring, Extracting, and Decoding Indicators of Cognitive Workload (MEDIC) system to automatically sense indicators of cognitive workload.

Under a previous Army contract, the company developed a system to improve training by Monitoring, Extracting, and Decoding Indicators of Cognitive Workload. MEDIC assists trainers by sensing indicators of cognitive workload, and offering insight into those factors and the trainee’s underlying performance. Under this new effort, Charles River will further develop this system, ruggedizing sensors and refining data processing to allow MEDIC to a communicate trainee’s state in real time across the variety of live training scenarios.

Trainers infer competence by observation, but in the field, combat medics who experience cognitive overload from inexperience or lack of skill may hesitate, make judgment errors, or fail to attend to critical details. Their skills need to be mastered down to an automatic response to avoid these missteps.

“We will improve this sensor to make it wireless and less obtrusive, as well as resistant to sand, sun, and other extreme environmental characteristics,” said Dr. Bethany Bracken, senior scientist at Charles River Analytics. “We are also enabling real-time sensing, processing, and expression of cognitive workload, increasing the accuracy for assessed human states, and providing an intuitive display of information to trainers in real time.”

MEDIC uses a device to determine brain blood oxygenation that is mounted inside the trainee’s helmet, as well as an armband with additional sensors to measure physical activity. As the training scenario unfolds, the trainer can take notes and record time-tagged photos and videos to record performance, which are then overlaid with the data from trainee-worn sensors.