Warfighters must maintain agility and performance in extreme conditions such as navigating rugged terrain, carrying heavy loads, and enduring prolonged exertion, often while facing unpredictable threats. Wearable technologies like robotic exoskeletons and advanced footwear have the potential to enhance warfighter performance and reduce injury risk. However, current design methods often rely on one-size-fits-all approaches and fail to account for how individuals adapt to these devices in real-world settings. This project addresses that gap by developing Digital Twins of agile locomotion in the form of personalized, data-driven simulations that model the complex and dynamic interaction between human movement, wearable technology, and the environment. By integrating real-time physiological and biomechanical data, these models enable better design, training, and deployment of active wearable technology to improve human agility. In addition to advancing national defense and security, this work has broad societal benefits to public health as the mathematical modeling techniques developed can also be used to improve wearable technology design for other user populations, such as those with motor impairment. The overarching goal of this project is to develop mathematical methods enabling an advanced Digital Twin model of human agile locomotion, aimed at optimizing the design of advanced footwear technology to enhance human agility and mobility. In order to accomplish this, this pr