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. We confirmed the need for machine-learning analytics to process biometric data and provide insights during our feasibility study in Phase I. The purpose of this Phase II proposal is to develop a prototype that can be utilized by the Air Force. The objectives of this proposal include developing predictive modeling, validating that modeling, enhancing the web application to facilitate communication between care providers and patients, and developing a mobile application to facilitate patient engagement. This capability is an extension of our (nondefense) commercial healthcare technology that collects real-time physiological data, calculates the health status of an individual, and securely communicates that information, including critical alerts, to the medical team. As evidence of commercial traction, Adyptation has secured commercial revenue and private investment capital, including closing additional investment since the since the initiation of our Phase I contract award. Our team has the technical experience to successfully complete this proposal and the commercialization experience to grow our business.