The Human Digital Twin (HDT) is a pivotal element in smart manufacturing systems geared towards Industry 5.0. HDT represents a digital manifestation of humans, aiming to innovate the integration between humans and systems by directly linking human characteristics to system design and performance. However, recent research lacks a standardized framework and architecture for HDT that can be applied across various real-world scenarios. This study introduces an approach that integrates 3D pose estimation through mono camera input and AR Glass, digitally twinning detailed work actions and human behavior of workers in a smart manufacturing environment. By facilitating effective monitoring of the work environment by managers, this approach contributes to boosting overall productivity. The paper discusses potential strategies to elevate the development of HDT and provides guidelines on possible directions and challenges for its advancement.

WiFi Channel State Information (CSI) represents the characteristics of wireless channels in wireless networks. WiFi CSI plays a pivotal role in wireless communications, primarily due to the variability of channel characteristics across time and space. By leveraging data analysis techniques based on Deep Neural Networks, we can capture the variations in channel characteristics associated with people's movements and behaviors indoors using WiFi CSI data. This offers a novel approach to Human Behavior Recognition, providing an alternative to camera-based methods that potentially infringe on privacy. In this paper, we delve into the structural design analysis of deep neural networks for human behavior recognition using WiFi CSI data and explore the training strategies vital for delivering extended services.