Customizing Human Machine Interfaces leveraging Digital Twins and Large Language Models

Industry 5.0 emphasizes human-centric manufacturing, where the operator needs to inform system design. While Operator Digital Twins monitor operator states through biometric data, existing approaches have not exploited this information to adapt industrial Human-Machine Interfaces (HMIs), which remain predominantly static and uniform. This paper proposes an architecture that extends Human Digital Twin systems with LLM-driven personalization for web-based HMIs. The framework collects operator-specific data through a mobile application integrating Health Connect and manual inputs. A Large Language Model interprets operator profiles containing biometric signals, permanent characteristics, and preferences, generating customized CSS stylesheets and configuration parameters that adapt visual properties and interaction modalities while preserving safety-critical elements. The architecture’s applicability is illustrated through diverse conceptual scenarios, such as visual impairments, protective equipment usage, elevated stress states, and ergonomic preferences. This demonstrates how a consistent operator profile format could drive varied interface adaptations to meet different operator needs.