The human digital twin for service management: Architecture and user modeling

Human Digital Twins (HDTs) are increasingly adopted across various domains, yet their application to network and service management remains limited. Nevertheless, HDTs offer significant potential for optimizing service configurations based on human behavior, preferences, and profiles. In this paper, we analyze the role of HDTs in network and service management, identifying key functionalities such as collaborative learning for user modeling, Quality of Experience (QoE) and emotion prediction, application personalization, and behavioral forecasting for network “what-if” analysis. We propose an architectural framework designed to monitor user status and generate a corresponding digital replica that interacts with other network components to enhance service delivery. Our solution integrates collaborative learning for QoE modeling and applies it to service optimization. By aggregating user data from multiple HDTs, the approach improves prediction accuracy and resource optimization. Extensive performance evaluations demonstrate that the proposed collaborative HDT framework enhances the final utility function that considers perceived quality and resource usage by 27% compared to non-collaborative methods.