This research-issue examines the role that Human Factors and Ergonomics （HFE） should play in addressing global-scale challenges. Traditionally, HFE has focused on human safety, health, efficiency, and comfort, developing through its emphasis on the interaction between humans and the objects or systems they engage with. In recent years, however, there has been a growing demand for broader contributions through a systems approach. In particular, achieving a sustainable society requires rethinking conventional frameworks and reconstructing the relationships among humans, the environment, society, and nature. This paper discusses the need to consider diverse stakeholders and explores both the potential and future challenges of HFE in this context.

Research and development is beginning to extract the causes of accidents and incidents and related human factors by analyzing document data published as accident investigation reports using natural language processing （NLP） and large-scale language models （LLM）. This paper discuss the potential for using artificial intelligence （AI） as a new methodology for improving the efficiency and quality of social technology system accident investigations, introducing the germinal research conducted by our research group. By having a GPT （Generative Pretrained Transformer） analyze the cause （factor） identification of a specific aviation accident report using the HFACS （Human Factors Analysis and Classification System）, we examined the better direction, limitations, and issues of applying generative AI to accident investigations, and reconsidered the role of the human factors domain.

We had the opportunity to reanalyze personality and behavioral scale data that had been accumulated over a quarter century. We made secondary use of 5 years of accumulated data on approximately 190,000 people to explore the relationship between each personality trait and error tendency and low validity. During this analysis process, there were several issues that required attention, such as ethical issues and the handling of the analysis results. Other issues that need to be considered in the future include opt-out and post-collection measures, and considerations for secondary use of research results such as means, number of Survey participants, and standard deviation.

Human Factors and Ergonomics （HFE） is a scientific discipline that contributes to the design of tasks, work, products, environments, and systems through the reciprocal interaction of theory and practice. Based on my experience as an editor of the Japanese Journal of Ergonomics and my perspective as an HFE professional, this paper organizes the current state of HFE’s theory and practice. It also identifies the challenges in promoting mutual enhancement and reciprocal interaction between the two and offers a forward-looking perspective on advancing the HFE to the next stage.

In high-mix, low-volume production, workers are required to acquire skills for various work procedures, standardization of work and promotion of proficiency are important challenges. This study develops a work analysis / management system using digital technologies to monitor assembly work in real time, collecting and analyzing daily work log data to visualize work progress, assembly time for each part and variations in assembly sequence. The system enables a detailed understanding of the current state of work, which was previously difficult to analyze, and supports identify work that requires improvement. The system also enables workers to evaluate differences in skill levels and variations in assembly work. As a result of the implementation of the system at actual production sites, it is shown that the findings can be used as new information that contributes to clarifying priorities for work improvement, standardization of work, and promotion of work proficiency. This study proposed the developed system as an effective work analysis / management method for high-mix, low-volume production, and verified its usefulness at actual production sites empirically.

The assessment of VDT （Visual Display Terminal） workers’ states is essential for their health management and optimizing the work environment. Autonomic nervous system indices, EEG, and eye movement-related indices are useful for assessing arousal levels and attention; however, conventional measurement methods require sensor or device attachment, which poses a burden on workers and leads to measurement deficiencies caused by work movements. Therefore, we developed a non-contact measurement method using a general-purpose RGB camera to simultaneously capture multiple indices. This report focuses on pulse wave analysis and implements an SVM-based method to detect falsely detected peaks for removal and completion. Data were collected and analyzed from 13 healthy adult males, who provided informed consent, during approximately 8 minutes of VDT work. The accuracy of pulse wave peak detection was evaluated by using peaks obtained from fingertip photoplethysmography as the ground truth, yielding a false positive rate of 5.85％ and a false negative rate of 5.61％. After applying the proposed method, the comparison of the mean pulse rate achieved an MAE of 0.70 bpm and an RMSE of 0.44 bpm, demonstrating performance suitable for practical use. These results indicate that the proposed method is useful for estimating the mean pulse rate without burdening VDT workers.

Technology to assess the state of VDT （Visual Display Terminal） workers is useful for improving work efficiency and health management. We developed a measurement method using an RGB camera, which imposes no burden on the user, with the aim of utilizing physiological responses that can capture the worker’s condition and unconscious reactions. While the first report focused on autonomic nervous system measures, this report centers on blinks and gaze estimation, which serve as indicators of concentration and cognitive load. As in the first report, data from 13 healthy adult males for 8 min of VDT work were included in the analysis. Facial feature points were extracted from facial video data using image processing, enabling the estimation of blink waveforms and gaze positions on the display. Blink detection evaluated using electrooculogram as the reference achieved better results than existing studies. For gaze estimation, waveforms showing the point and direction of gaze point were obtained, however the target accuracy for gazing region estimation was not achieved. The proposed method’s advantages include real-time processing using only a CPU without requiring specialized hardware and the ability to estimate blink waveforms from low-resolution videos. These results indicate that this method has high practicality for on-site use.

This study aims to comprehensively identify resilience competencies in the aviation domain using a large volume of near-miss event data collected through the Aviation Safety Reporting System （ASRS）. The analysis was conducted using BERT （Bidirectional Encoder Representations from Transformers）, a natural language processing method that captures context by learning word meanings through pretraining. Using 575 free-text narrative reports from ASRS, a model was constructed to extract resilience competencies while considering the four potentials of resilience （anticipating, monitoring, responding, and learning）. This model was then applied to analyze 42,938 reports. Sentences identified as containing each resilience potential （anticipating, monitoring, responding, and learning） were vectorized using Sentence-BERT, and k-means clustering was applied. By interpreting the meaning of each cluster, 31 resilience competencies utilized by aviation personnel to prevent near-miss events from escalating into accidents were identified. The comprehensiveness of these resilience competencies was confirmed through comparisons with related prior studies. Additionally, this study discusses practical examples of how these identified resilience competencies can be applied in operational settings.