Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science in Operations Research
Department
Department of Operational Sciences
First Advisor
Chancellor Johnstone, PhD
Abstract
This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military forces. The findings highlight the potential of machine learning in operational readiness, suggesting future directions for expanding wearable device data integration.
AFIT Designator
AFIT-ENS-MS-24-M-091
Recommended Citation
O, Sung Yong, "Federated Analysis of Wearables Data for United States Air Force Mental and Physical Readiness" (2024). Theses and Dissertations. 7725.
https://scholar.afit.edu/etd/7725
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.