Artificial intelligence in occupational ergonomics: A bibliometric analysis
Rheina Khaisa Rhehani Putri, Achmad Pratama Rifai, Manik Mahachandra, Zulkifli Tahir, Auditya Purwandini Sutarto, ArdiyantoBackground
The rapid growth of artificial intelligence (AI) offers significant opportunities to enhance occupational ergonomics practices. AI can improve ergonomic processes through data-driven and automated approaches for identifying, assessing, and mitigating workplace risks. However, a comprehensive synthesis that maps scientific productivity, influence, knowledge structure, and future trends related to AI in occupational ergonomics remains limited.
Objective
This bibliometric analysis examines scientific productivity, influence, knowledge structure, and emerging research trends in the application of AI to occupational ergonomics.
Methods
Publications indexed in the Scopus database were retrieved using a predefined keyword strategy. Performance analysis was conducted to evaluate scientific productivity and influence across authors, journals, conference publishers, countries, institutions, and individual publications. In addition, science mapping was performed to examine the knowledge structure and future research trends through keyword analysis, including popular keywords, citation burst detection, and keyword co-occurrence visualization.
Results
Research on AI in occupational ergonomics has increased rapidly since the late 2010s. Leading contributions originated from both ergonomics- and technology-focused journals and conferences. The United States and several European countries dominate research output. The literature primarily focuses on risk assessment, particularly related to musculoskeletal disorders. Wearable sensors, computer vision, and machine learning emerged as key technological enablers. Recent studies increasingly address contemporary industrial revolution contexts.
Conclusions
This bibliometric analysis provides an overview of the evolving research landscape of AI in occupational ergonomics, highlighting dominant themes and directions. The findings offer a foundation for future research aimed at developing intelligent, human-centered ergonomic solutions to support worker well-being and organizational performance.