A Human-Digital Twin model to track human motion in an experimental Cyber-Socio-Technical System

Industry 4.0 radically changed the scenario of almost all manufacturing systems by implementing strong technological changes that led to several potential benefits, i.e., AI and IoT. On the other hand, the exposure to digital threats creates the need to study ways to make those systems capable of reacting to new menaces. RESIST Project, i.e. RESilience management to Industrial Systems Threats, of which this working paper is part, aims to assess the resilience factor of Cyber-Socio-Technical System plants. The work presented here focuses on the human part of that system and has the goal of defining a methodology to build a Human-Digital Twin model of the operator working inside the plant. The methodology involves the use of control volumes and motion capture technologies to give access to a flexible and customizable framework tracking the operator's movements. Finally, this contribution gives an overview of the experimental recording campaign and of future work.