A Human-Centric AutomationML Framework for Adaptive UX-Driven Automation

The transition to Industry 5.0 emphasizes human-centric automation, prioritizing ergonomics, cognitive adaptability, and real-time collaboration between humans and machines. Traditional automation strategies have focused primarily on efficiency and standardization, often overlooking human operators’ cognitive and physiological workload. This paper presents an AutomationML-based (AML) digital framework that integrates user experience (UX) approaches and human-in-the-loop methodologies to optimize human task design by harmonizing human cognitive states, physiological data, and real-time behavioral insights from industrial digital assets and the physical factory. The presented approach extends the recently defined AML standard by introducing a structured human taxonomy, a UX database architecture, and an enhanced task modeling and adaptation strategy based on the operators’ feedback. An industrial use case about quality inspection is presented to validate the proposed framework, where an assisted inspection system integrates augmented reality visualization with human physiological monitoring using a sensorized wristband. This framework enables real-time task adaptation based on the estimate of the operator’s workload, reducing cognitive overload and optimizing task allocation between humans and machines. Results on the use case suggest how UX-driven automation improves both process performance and workers’ well-being, as well as the overall system flexibility. This research contributes to the development of ergonomic and cognitive-aware automation solutions that enhance collaboration between humans and machines.