A Military Human Performance Management System Design using Machine Learning Algorithms

The area of human performance improvement has become a greater focus in recent military contexts as evidenced by Australian Defence Force projects. The aim of the design proposed in this paper is to develop a Performance Management System using Machine Learning (PMSML) to enhance the physical human performance of individual warfighters in combat situations through 1) early recognition and self-management of acute health events in the field; 2) forecasting of soldier (user) failure; and 3) proactive self-management of longer-term health outcomes during prolonged manoeuvres or combat situations. This paper proposes a high-level design and approach using machine learning algorithms to assess the feasibility of improving metrics such as health data accuracy and efficiency when transmitting data from sensors to the cloud in military networks. The significance of this design is to predict health conditions of users on a personalised basis for an individual's physical and mental health performance without compromising performance metrics using machine learning algorithms. Results show that machine learning algorithms outperformed other existing methods, which must compromise between certain metrics.