This study presents a novel architecture for Human Digital Twins (HDT) applied in manufacturing, designed to address several challenges in the current industry, including data management, safety, intelligent assistance, and operator training. We analysed various existing proposals, highlighting the need for adaptable and scalable architectures that support the complexity of industrial processes. Our proposed architecture integrates and processes multimodal data to facilitate the incorporation of human related information, as emotion detection, fatigue identification, and intelligent monitoring, among others. These technologies that are embedded in our proposal are crucial for improving operational efficiency and safety in the workplace. The proposed architecture stands out for its ability to adapt to various industrial applications, making it a viable solution for implementing digital twins in cyber-physical environments.