Thanks to synchronized virtual representation, digital twins are a means to produce analyses, predictions and optimizations of real-world systems. However, some of these systems tightly engage with humans so that the role of the latter is determining in the system’s operation. This is for example the case in contexts such as industry 5.0 or the management of the control of critical systems, where the quality of collaboration between humans and machines will depend on the anticipation of their respective actions, interactions and decisions. Thus, to improve the accuracy of predictions and expand the applicability in various fields, it is necessary, based on knowledge from the human and social sciences, to develop digital twins that account for the complexity and richness of human behaviors (decision-making processes, interactions, emotions, etc.). These behavioral models may notably rely on machine learning, data mining, agent-based modeling and knowledge engineering. After having identified the useful human behavior models, we will study their conceptual articulation and their technical integration with the models of cyber-physical entities in the digital twin system. Additionally, we will explore how digital twin services are impacted and can be revised to account for these human-centric aspects. Finally, we will evaluate the effectiveness of human-centric digital twins in various applications by implementing experiments on representative real cases.
This research work aims to make the following contributions:
• The development of an approach based on human behavior models to achieve human-centric digital twins.
• New knowledge on the impact of human behavior on the control of a system and vice versa.
• Practical applications and guidelines for using human-centric digital twins in real-world scenarios.
This PhD will be carried out at Grenoble.