Publication Date
2025
Document Type
Thesis
Committee Members
Michael L. Raymer, Ph.D. (Advisor); Krishnaprasad Thirunarayan, Ph.D. (Committee Member); Thomas Wischgoll, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Recent advances in wearable technology allow continuous monitoring of physiological and behavioral data, opening new opportunities for real-time assessments of readiness and well-being. However, creating predictive models that generalize across diverse users remains challenging, especially in high-stakes settings like the military, where preventable injuries, illnesses, and stress-related performance declines are frequent. This research assesses the feasibility of using supervised machine learning models trained on wearable device data to predict subjective readiness indicators—recovery, stress, injury, and illness. Data from over 10,000 users in the OHWS (Optimizing the Human Weapons System) program combined daily check ins with physiological metrics from Garmin, Polar, and Oura devices. After thorough preprocessing and feature engineering, such as using SMOTE for handling class imbalances, models were created and compared to off-the-shelf models from Gemini Sports Analytics. Future work will focus on refining automation pipelines and improving model interpretability to enable operational deployment for real-time readiness assessment and injury prevention in military and athletic populations.
Page Count
119
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2025
Copyright
Copyright 2025, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
