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Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task.
Rao, Hrishikesh M; Smalt, Christopher J; Rodriguez, Aaron; Wright, Hannah M; Mehta, Daryush D; Brattain, Laura J; Edwards, Harvey M; Lammert, Adam; Heaton, Kristin J; Quatieri, Thomas F.
Affiliation
  • Rao HM; Human Health and Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, United States.
  • Smalt CJ; Human Health and Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, United States.
  • Rodriguez A; Human Health and Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, United States.
  • Wright HM; Human Health and Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, United States.
  • Mehta DD; Human Health and Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, United States.
  • Brattain LJ; Human Health and Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, United States.
  • Edwards HM; Human Health and Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, United States.
  • Lammert A; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States.
  • Heaton KJ; Military Performance Division, U.S. Army Research Institute of Environmental Medicine, Natick, MA, United States.
  • Quatieri TF; Human Health and Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, United States.
Front Hum Neurosci ; 14: 222, 2020.
Article in En | MEDLINE | ID: mdl-32719593
ABSTRACT
Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we developed a simulated marksmanship scenario with an embedded working memory task in an immersive virtual reality environment. As participants performed the marksmanship task, they were instructed to remember numbered targets and recall the sequence of those targets at the end of the trial. Low and high cognitive load conditions were defined as the recall of three- and six-digit strings, respectively. Physiological and behavioral signals recorded included speech, heart rate, breathing rate, and body movement. These features were input into a random forest classifier that significantly discriminated between the low- and high-cognitive load conditions (AUC = 0.94). Behavioral features of gait were the most informative, followed by features of speech. We also showed the capability to predict performance on the digit recall (AUC = 0.71) and marksmanship (AUC = 0.58) tasks. The experimental framework can be leveraged in future studies to quantify the interaction of other types of stressors and their impact on operational cognitive and physical performance.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Hum Neurosci Year: 2020 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Hum Neurosci Year: 2020 Document type: Article Affiliation country: United States