Machine Learning for Airman Health and Readiness

Airmen need to be at an ideal physiological state, without undue fatigue or stress, to perform their mission. To track the state of the Airman we need to understand and interpret data streams from wearables, trackers and other sensors. This proposal will assess the feasibility of integrating our machine learning analytics to track Airman health and readiness. Our commercial healthcare technology collects real-time physiological data, calculates the health status of an individual, and securely communicates that information, including critical alerts, to the medical team. The objectives of this proposal include: 1) customer discovery of Airman health tracking needs; 2) market analysis of high-fidelity physiological tracking devices; 3) technical assessment of integration requirements with AF platforms; and 4) final report on machine learning analytics for Airman health and readiness tracking. The opportunity to conduct a Phase I feasibility study will allow us to define the AF customer need, address the technical feasibility of our solution and assess the financial sustainability of our technology. The goal of this effort is to identify AF end user(s) and customer(s) interested in our solution and find avenues (e.g. SBIR Phase II award) to develop a prototype and transition our technology to support the AF mission.