Seunghyo Beak

1. Introduction In recent years, the field of real-time human pose estimation has rapidly advanced, driven by the introduction of cutting-edge techniques such as deep learning which has significantly improved the field of real-time human pose estimation [1,2,3]. This technology has direct implications for enhancing the safety management of field workers by serving as a valuable tool for accident prevention and rapid response in the workplace [1,2].

1. Introduction Pandemics and other forms of infectious disease outbreaks are unique examples of manufacturing risk, characterized by high uncertainty, increased contagion, and long-term disruption to manufacturers, supply chain actors, end users, and consumers [1]. The outbreak of the COVID-19 pandemic has had an enormous impact on various sectors, including manufacturing.