Developing AI enabled sensors and decision support for military operators in the field

• Published in: Journal of science and medicine in sport Vol. 26; pp. S40 - S45
• Main Authors: Russell, Brian K., McGeown, Josh, Beard, Bettina L.
• Format: Journal Article
• Language: English
• Published: Australia Elsevier Ltd 01.06.2023; Elsevier Limited
• Subjects: Accuracy; Artificial Intelligence; Cognition performance; Decision making; Decision Support; Decision support systems; Deep learning; Fatigue; Heart rate; Human performance; Load; Physiology; Remote physiological monitoring; Sensors; System theory; Trouble shooting; Cognition performance; Remote physiological monitoring; Artificial Intelligence; Decision Support
• ISSN: 1440-2440; 1878-1861; 1878-1861
• DOI: 10.1016/j.jsams.2023.03.001

Wearable sensors enable down range data collection of physiological and cognitive performance of the warfighter. However, autonomous teams may find the sensor data impractical to interpret and hence influence real-time decisions without the support of subject matter experts. Decision support tools can reduce the burden of interpreting physiological data in the field and incorporate a systems perspective where noisy field data can contain useful additional signals. We present a methodology of how artificial intelligence can be used for modeling human performance with decision-making to achieve actionable decision support. We provide a framework for systems design and advancing from the laboratory to real world environments. The result is a validated measure of down-range human performance with a low burden of operation.