A performance map framework for maximizing soldier performance
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Soldiers in the Unites States Army operate under uniquely demanding conditions with increasingly high performance expectations. Modern missions, including counter-insurgency operations in Iraq and Afghanistan, are complex operations. The Army expects this complexity to continue to increase. These conditions affect Soldier performance in combat. Despite spending billions of dollars to provide Soldiers with better equipment to meet the demands of the modern battlefield, the U.S. Army has dedicated comparatively little resources to measuring and improving individual Soldier performance in real-time. As a result, the Army does not objectively measure a Soldier’s performance at any point in their active duty career. The objective of this report is to demonstrate the utility and feasibility of monitoring Soldier performance in real-time by means of visual 3D performance maps supported by a Bayesian network model of Soldier performance. This work draws on techniques developed at the University of Texas’ Robotics Research Group for increasing performance in electro-mechanical systems. Humans and electro-mechanical systems are both complex and demonstrate non-linear performance trends which are often ignored by simplified analytical models. Therefore, application of empirical Bayesian models with visual presentation of data in 3D performance maps enables rapid understanding of important performance parameters for a specific Soldier. The performance maps can easily portray areas of non-linear performance that should be avoided or exploited, while presenting levels of uncertainty regarding the assessments, thus empowering the individual to make informed decisions regarding control and allocation of resources. The present work demonstrates the utility of visual performance maps by structuring 19 relatively mature 3D performance maps based on published empirical research data and analytical models related to human performance. Based on a broad review of the literature, the present research evaluated 10 potential physiological indicators, termed biomarkers that correlate with human responses to a select set of stressors, referred to as impact parameters. The 10 evaluated impact parameters affect various components of Soldier performance. The present research evaluated the documentation of these relationships in the existing literature with regard to 9 general Soldier performance measures. Identifying the research supported relationships from biomarkers to impact parameters to Soldier performance measures resulted in a preliminary Bayesian Soldier Performance Model, from which it is possible to create 70 distinct 3D performance maps. Based on the quality of the relationships identified in the reviewed literature, and a contemporary evaluation of existing sensor technology for the related biomarkers, the present research assessed 26 of the potential 70 performance maps as being achievable in the near-term. Continuing development of the Soldier Performance Model (SPM) as proposed in this report has the potential to increase Soldier performance while simultaneously improving Soldier well-being, reducing risk of physical and mental injury, and reducing downstream treatment cost.
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