Bracken, B.1, Festa, E.2, Sun, H.2, Leather, C.1, Strangman, G.3, Palmon, N.1, Pacheco, M.4, Silva, F.4, and deB Frederick, B.5
Presented at the 2018 Brain Health and Performance Summit, Columbus, OH (April 2018)
Army medic training often includes high-fidelity simulations. Trainers currently infer competence by observation alone—a challenging task. Assessing cognitive workload using functional near-infrared spectroscopy (fNIRS) when individuals are seated is well established. When cognitive workload increases, there is a corresponding increase in prefrontal blood flow that correlates with increased task engagement. Once the task becomes too difficult, there is a decrease in blood flow that correlates with disengagement from the task and decreased performance (Bunce, et al., 2011; Ayaz, et al., 2012). However, fNIRS sensor devices that can be used to assess cognitive workload during normal activities (e.g., combat medic training) are only recently emerging. Standard sensors are large (e.g., full-head), required heavy equipment (e.g., batteries, laptops), and expensive (~$10K).
As part of a Charles River Analytics Army-funded SBIR effort to develop a system for augmenting training by Monitoring, Extracting, and Decoding Indicators of Cognitive Workload (MEDIC), now its second Phase II, we designed a portable, cost-effective fNIRS sensor.
The sensor resulting from the first Phase II was designed to be mounted on the inside of a baseball cap or standard issue helmet to measure indicators of cognitive workload. It is now commercially available. During the next phase of this project, we are refining this sensor, decreasing size and cost; increasing ruggedness (e.g., resistance to sand/water), portability, and comfort. The sensor resulting from this second Phase II planned to be ready for data collection in November 2018.
This work has resulted in a commercially-available fNIRS device useful for assessing cognitive workload (when paired with appropriate data analytics) in real-world environments. New work is improving on this sensor, improving size, comfort, ruggedness, and cost, making fNIRS available for use in even more strenuous environments.
1 Charles River Analytics, Cambridge, MA
2 Brown University, Providence, RI
3 Massachusetts General Hospital, Charlestown, MA
4 Plux Wireless Biosignals, Lisbon, Portugal
5 McLean Hospital/Harvard Medical School, Belmont, MA
This work was supported by United States Army Medical Research and Materiel Command under Contract Nos. W81XWH-14-C-0018 and W81XWH-17-C-0205 and NASA Contract Nos. NNX15CJ17P and NNX16CJ08C. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Army Medical Research and Materiel Command. In the conduct of research where humans are the participants, the investigators adhered to the policies regarding the protection of human participants as prescribed by Code of Federal Regulations (CFR) Title 45, Volume 1, Part 46; Title 32, Chapter 1, Part 219; and Title 21, Chapter 1, Part 50 (Protection of Human Participants).
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