Abstract
My technical capstone project and my STS research are connected through their shared focus on hemorrhage-control training and the role of sensor-based technologies in defining effective performance. In my capstone project, my team and I developed a wearable moulage system that simulates deep wound bleeding and provides real-time feedback on wound packing performance. This system uses sensors and predefined thresholds to determine whether a trainee has successfully controlled bleeding. To better understand how these thresholds come to define “success,” my STS research examines the U.S. Army’s Smart Surrogates evaluation event using the Social Construction of Technology (SCOT) framework. While my capstone focuses on building a training device, my STS research explores how different groups shape what counts as effective training.
My technical project addresses a major limitation in current hemorrhage-control training. Existing methods rely heavily on static manikins and instructor judgment, which do not provide objective feedback on whether a trainee’s technique would actually stop bleeding. Our device introduces a wearable system with embedded sensors that measure pressure, packing depth, and time under pressure. These values determine whether the trainee has reached a threshold that represents successful hemorrhage control. The system provides real-time feedback to guide the user and reinforce proper technique. The goal is to make wound packing a measurable and repeatable skill that can be consistently evaluated.
My STS research examines how these measurable standards emerge and become accepted. Using the SCOT framework, I argue that Smart Surrogate technologies were developed through negotiation between different social groups, including military leadership, engineers, and instructors. Each group prioritized different aspects of training, such as realism, scalability, or measurable performance. Over time, these perspectives converged around quantifiable metrics like pressure thresholds and time-to-control, which became stabilized as the dominant definition of success. This shift improves standardization and accountability while also raising ethical concerns, particularly the risk of overreliance on metrics and the potential for overconfidence when measured performance is assumed to translate directly to real-world outcomes.
Working on these two projects at the same time helped me better understand the role of engineering decisions in shaping real-world outcomes. My STS research pushed me to think more critically about how we defined performance thresholds in our capstone project. I realized that these values are influenced by assumptions about what effective hemorrhage control looks like in high-stress environments. As a result, our team focused on incorporating more realistic conditions into our thresholds instead of relying only on idealized values. This experience reinforced that engineering design involves defining success in ways that carry real ethical and practical consequences. The thresholds we build into systems do more than measure performance; they shape how people act when the stakes are highest.
