Text Mining Analysis of 2809 Articles on Fatigue Data Acquisition Methods for Traffic-Related Personnel: A Systematic Review Leveraging Natural Language Models

Abstract

Fatigue among transportation-related personnel is a critical factor impacting traffic safety. In fatigue detection studies, data acquisition plays an essential role in determining the accuracy and reliability of the results. The data acquisition methods used in existing studies vary significantly and include electroencephalography, eye activity monitoring, and heart rate change analysis, each with distinct advantages and limitations. Such methodological diversity necessitates a systematic evaluation of their applicability and performance. Given the growing body of research in fatigue detection, traditional qualitative reviews face considerable challenges in covering the breadth of available information. To address this gap, this article proposes a structured information extraction framework leveraging a large language model to systematically evaluate 2809 studies on fatigue detection published between 2000 and May 2025. The developed framework achieved high accuracy in article screening (95.62%) and demonstrated robust performance in generating structured text. Our findings show an almost equal distribution between contact-based (50.24%) and noncontact-based (49.76%) detection methods, with parallel developmental trends and a strong positive correlation (r = 0.9569). Temporal trends indicate substantial research growth after 2012, coinciding with advancements in sensor technologies and computational capabilities. Key methodological combinations include eye–face signals (186 instances) for behavioral detection and brain–heart signals (102 instances) for physiological monitoring. Our findings serve as a foundational reference for selecting appropriate methods, and the proposed framework demonstrates potential applicability across diverse scientific disciplines.