Towards Human Digital Twins to enhance workers' safety and production system resilience

Industry 5.0 complements Industry 4.0 aiming to create a sustainable, human-centered, and resilient industry. In this context, enabling technologies, such as artificial intelligence, internet of everything, and digital twins, can be used to monitor and enhance the workforce to improve the efficiency and resilience of the entire manufacturing system. By developing socio-technical digital twin architectures, companies will be able in the short future to monitor machines, products, and workers' real-time states as a whole ecosystem. In this study, the authors focus their attention on human digital twin solutions for manufacturing systems, enabling dynamic scheduling of jobs by minimizing the makespan and considering a set of workers’ parameters that are continuously monitored through an ergonomic digital platform. This paper proposes the architecture of a real-time monitoring system and how it can help detect awkward postural behavior or unbalanced workload among workers, according to their individual features. At the same time, the system interacts with the human digital twin system which proposes a rescheduling of the jobs whenever it is necessary. Finally, a discussion on the practical limitations of human digital twin implementations in industrial environments is provided.