Bibliometric Mapping of Human-Machine Interfaces in Autonomous and Intelligent Systems

Abstract

The growing integration of autonomous systems in industry has reinforced the demand for efficient Human-Machine Interfaces (HMIs) to ensure safe and adaptive interactions. This study presents a bibliometric analysis of 99 publications (2015–2025) on supervisory interfaces, using Scopus data and tools such as VOSviewer and Bibliometrix. The results revealed six main clusters, centered on terms like human-machine interface, process control, machine learning, and decision-making. The analysis highlights the leadership of China, the United States, and India, while showing the absence of Brazilian contributions. A marked rise in publications occurred after the COVID-19 pandemic, although gaps persist in applied studies involving biosystems.