Emulating Reactive Workloads for Cyber-Human Systems: A Data-Driven Methodology

Wearable Cognitive Assistance (WCA) has the potential to revolutionize daily life with real-time, context-aware guidance, but current performance models often overlook the dynamic nature of human-system interactions, leading to inefficiencies in resource allocation and system responsiveness. In this work, we investigate the implications of the correlated nature of human-system interactions through the development of a novel data-driven methodology for the modeling of task execution times in WCA. We apply this methodology to a WCA application previously shown to exhibit second-order effects between system responsiveness and human performance. Our resulting model presents an improvement in up to 30% with respect to traditional first-order approaches, highlighting the importance of capturing complex behavioral dynamics. These findings raise important questions about the design and optimization of WCA systems and the tools that target them: What are the implications of this correlation for resource allocation and system design in real-world deployments? How can our methodology inform the development of more accurate and adaptive models for WCA applications? By exploring these questions, this research aims to contribute to the development of more efficient and effective WCA systems.