Search Results (82)

Seatbelt use is well established as a life-saving measure. Nevertheless, many drivers and passengers continue to neglect seatbelt use. This study examines the risks associated with unrestrained occupants involved in motor vehicle crashes. Using data from the Fatality Analysis Reporting System from 2000 to 2018, the relative risk of fatal traffic accidents for unrestrained vehicle occupants in the United States was estimated using the maximum likelihood estimation method. The findings indicate that unrestrained passengers make up about 12% of all passengers on the road and face a roughly 4.3 times greater likelihood of fatality in severe crashes. Additionally, unrestrained drivers, whose higher risk profiles are linked not only to their lack of restraint but also to broader patterns of hazardous driving behavior, account for over 8% of all drivers and exhibit a risk approximately 5.4 times higher in causing fatal crashes compared to restrained drivers. The findings of this study reveal the prevalence and consequences of unrestrained vehicle occupants and supports ongoing efforts to promote seatbelt utilization and bolster road safety protocols. By doing so, we can alleviate the burden of preventable injuries and fatalities on individuals, families, and society at large, thus fostering a safer and more secure transportation environment for all. Full article

The construction industry is one of the most hazardous occupational sectors globally, with persistently high rates of worker injuries and fatalities. This study examined the association between safety motivation and safety climate among construction workers, addressing a critical gap in understanding their bidirectional relationship. A cross-sectional survey was administered to 922 construction workers across multiple commercial projects within a single U.S. state, yielding 383 valid responses (41.5% response rate). The survey instrument measured safety motivation types (intrinsic, extrinsic, and negative) and multiple safety climate dimensions, including leadership and communication, safety procedures and training, peer support, recognition, and equipment availability. The results revealed that safety motivation demonstrated a significant positive correlation with overall safety climate (r = 0.467, p < 0.01), with leadership and communication showing the strongest association (r = 0.514, p < 0.01). Analysis of motivation types indicated that negative motivation (fear of accidents) predominated (41%), followed by extrinsic (34%) and intrinsic motivations (25%). The findings support a reciprocal relationship wherein safety motivation and safety climate mutually reinforce one another, influencing safety performance and outcomes. The study highlights the need for safety interventions that simultaneously address organizational climate factors and diverse individual motivational pathways to improve safety performance in the construction industry. Full article

Occupational safety analytics is increasingly moving toward data-driven methodologies; however, existing models often struggle to capture the multidimensional nature of accident causation. This study presents a multimodal Hybrid Transformer-LSTM framework for classifying occupational fatalities by jointly modeling unstructured narratives, cyclical temporal features, and regional spatial indicators. Utilizing a large-scale dataset of 14,914 OSHA fatality records, the proposed architecture leverages BERT-based embeddings for semantic extraction and Bidirectional LSTMs as non-linear pattern encoders for spatiotemporal context. Conceptually grounded in the Swiss Cheese Model, the framework treats different data modalities as proxies for distinct layers of system risk, ranging from proximal unsafe acts to environmental preconditions. Experimental results show that the multimodal architecture achieves an accuracy of 84.56%, representing a 5.33% gain over unimodal BERT baselines. To address the inherent “black-box” nature of deep learning, a SHAP-based explainability framework is incorporated to quantify the contributions of both textual tokens and environmental features to the model’s decision-making process. The results indicate that integrating narrative semantics with temporal and spatial context enhances discriminative performance and enables context-aware classification within a weakly supervised setting. By providing a scalable and interpretable classification framework, this study offers a data-driven decision-support approach for safety professionals and regulatory bodies seeking to implement evidence-based risk management strategies in high-risk industrial sectors. Full article

Background: Work at heights is a high-risk occupational activity, with falls being a leading cause of fatal accidents in construction and industrial maintenance. Conventional safety training often does not fully prepare workers for real-world hazards. Immersive virtual reality (IVR) has emerged as a promising training tool, providing controlled and realistic simulations of hazardous scenarios. This hypothesis-generating pilot study evaluates the feasibility and effectiveness of IVR in enhancing practical skills, safety perception, and physiological responses during work-at-height training. Methods: This controlled trial will recruit first-time trainees from the National Learning Service (SENA) of Colombia. Participants will be assigned to an intervention group, receiving IVR training before field-based practical sessions, or a control group, receiving standard theoretical instruction. Outcomes include practical skill acquisition, ergonomic risk, cognitive performance, and physiological responses, including heart rate variability measured with validated devices. Assessments will be performed using standardized tools, and data will be analyzed with repeated-measures ANOVA and regression models to compare groups. Conclusions: By integrating practical, cognitive, ergonomic, and physiological measures, this study will provide evidence on whether IVR improves the effectiveness of work-at-height training beyond conventional methods. Findings may inform future strategies to enhance occupational safety training in high-risk work environments. Full article

The aim of this article is to develop a machine learning (ML)-based predictive model for industrial accidents in the energy sector. The dataset used in this study was obtained from the Kaggle platform and consists of summaries derived from reports of occupational incidents resulting in injuries or deaths between 2015 and 2017. A total of 4739 accident cases were included, containing information on accident date, accident summary, degree and nature of injury, affected body part, event type, human factors, and environmental factors. Six supervised machine learning models—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost)—were developed and compared to identify the most suitable model for the data. Model performance was evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC), which were selected to ensure reliable prediction in safety-critical accident scenarios. The results indicate that XGBoost and GBDT achieve superior performance in predicting human and environmental risk factors. These findings demonstrate the potential of machine learning for improving safety management in the energy sector by identifying risk mechanisms, enhancing safety awareness, and providing quantitative predictions of fatal and non-fatal accident occurrences for integration into safety management systems. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

Drowsiness constitutes a significant risk factor in diverse occupational settings, including healthcare, industry, construction, and transportation, contributing to accidents, injuries, and fatalities. Electroencephalography (EEG) signals, offering direct measurements of brain activity, have emerged as a promising modality for drowsiness detection. However, the inherent non-stationary nature of EEG signals, coupled with substantial inter-subject variability, presents considerable challenges for reliable drowsiness detection. To address these challenges, this paper proposes a hybrid approach combining convolutional neural networks (CNNs), which excel at feature extraction, and support vector machines (SVMs) for drowsiness detection. The framework consists of two modules: a CNN for feature extraction from EEG scalograms generated by the Continuous Wavelet Transform (CWT), and an SVM for classification. The proposed approach is compared with 1D CNNs (using raw EEG signals) and transfer learning models such as VGG16 and ResNet50 to identify the most effective method for minimizing inter-subject variability and improving detection accuracy. Experimental evaluations, conducted on the publicly available DROZY EEG dataset, show that the CNN-SVM model, utilizing 2D scalograms, achieves an accuracy of 98.33%, outperforming both 1D CNNs and transfer learning models. These findings highlight the effectiveness of the hybrid CNN-SVM approach for robust and accurate drowsiness detection using EEG, offering significant potential for enhancing safety in high-risk work environments. Full article

Ensuring employee safety is a top priority for every enterprise, and it is especially critical in high-risk industries like coal mining. To achieve this goal, it is essential to focus efforts on identifying existing hazards and thoroughly assessing the associated risks. Accurate identification and detailed characterization of occupational hazards play a pivotal role in the occupational risk assessment process, providing the foundation for effective safety strategies. This article presents an analysis of the process of identifying occupational hazards in hard coal mining, based on applicable legal regulations and a review of the relevant literature. The analysis reveals, on the one hand, a diversity of approaches to hazard classification, and on the other, a limited use of the characteristic features of hazards in classification processes. The findings of this review form the basis for proposing a systematic classification of occupational hazards in hard coal mining, taking into account the specific features of hazards in relation to their sources and potential consequences. The proposed classification not only categorizes hazards but also describes the specifics of hazard sources, such as environmental conditions, machinery, chemicals, and human factors, as well as the possible outcomes of these hazards, including physical injury, health impacts, and even fatalities. The aim of this article is to present a proposed classification of occupational hazards in hard coal mining and to provide a detailed characterization of these hazards based on the description of their sources and potential consequences. The proposed approach, grounded in the identification of characteristic features of hazards, facilitates the effective selection of preventive measures that can be implemented to reduce risk and improve workplace safety. Due to the presence of the full spectrum of natural hazards in Polish hard coal mining, the analysis draws on available statistical data, focusing on those hazards that contribute most significantly to fatal accidents and serious injuries. In conclusion, the article emphasizes the importance of a structured and systematic approach to identifying and assessing occupational hazards in the coal mining industry. By drawing on legal and literature-based insights, it aims to contribute to the development of more effective safety practices that protect workers and minimize the occurrence of workplace accidents and illnesses. Full article

This study applied data analysis techniques to analyze work-related accidents in Brazil’s mining sector from 2019 onward, identifying key risks and patterns. Using public datasets from governmental sources, it categorized accidents by the type of injury, causal agents, and affected body parts. The methodology employed included data cleaning, processing, and the development of interactive visualizations using advanced analytical tools, such as Python and Power BI, to facilitate data interpretation. Among the most significant events, the Brumadinho tailings dam collapse in 2019 emerged as a major outlier, substantially affecting multiple aspects of the analysis. This single incident accounted for 71.7% of all work-related fatalities recorded during the four-year period under study, highlighting its disproportionate impact on the dataset. This study also examined the main causes and consequences of mining accidents and facilitated the creation of victim profiles based on gender and age group, incorporating psychological theories regarding risk perception. It was concluded that, although the mining sector represents a small fraction of all work-related accidents in Brazil, the proportion of accidents relative to the number of workers in the sector is substantial, highlighting the need for stricter occupational safety management. The results can guide regulations and help companies and institutions to create safer, more sustainable mining policies. The methodology proved to be highly suitable, indicating its potential for application in safety analysis across other sectors. Full article

Agriculture and manufacturing report the highest rate of occupational accidents and fatalities in Italy. Safety training provided through digital devices has been shown to be effective in promoting safety behaviors at work. This study aimed to investigate through a questionnaire the perceptions of working conditions, risks in using machines, and interest in using digital devices for safety training purposes in a group of vineyard workers (VWs, N = 40) and manufacturing workers (MWs, N = 39) in Northwest Italy. Referring to working conditions, VWs significantly differ compared to MWs (p < 0.05) in fatigue perception, repetitiveness, quantity and definition of tasks compared to the available time, work pace definition, and level of communication. Tractors and lathes were considered the most hazardous machinery for VWs and MWs, respectively. For both groups, workers’ age negatively correlated with digital device use (r = −0.399 p < 0.05 for VWs, r = −0.673 p < 0.01 for MWs) but not with interest in using them. Device adoption positively correlated with the perceived importance of gamification content (r = 0.193 and r = 0.164, p > 0.05 for VWs and MWs, respectively), but the video lessons reported a higher mean score by both groups as preferred content. These findings suggest that digital safety training requires customized content to effectively adapt to different productive sectors. Full article

High-risk industries like mining must address occupational safety to reduce accidents and fatalities. Training through role-playing, simulations, and Serious Games (SGs) can reduce occupational risks. This study aims to conduct a systematic literature review (SLR) on SG methodologies for the mining sector. This review was based on a methodology inspired by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Three research questions were formulated to explore how SGs contribute to immediate feedback, brain stimulation, and training for high-risk scenarios. The review initially identified 1987 studies, which were reduced to 30 relevant publications following a three-phase process: (1) A search string based on three research questions was defined and applied to databases. (2) Publications were filtered by title and abstract. (3) A full-text reading was conducted to select relevant publications. The SLR showed SG development methodologies with structured processes that are adaptable to any case study. Additionally, it was found that Virtual Reality, despite its implementation costs, is the most used technology for safety training, inspection, and operation of heavy machinery. The first conclusion of this SLR indicates the lack of methodologies for the development of SG for training in the mining field, and the relevance of carrying out specific methodological studies in this field. Additionally, the main findings obtained from this SLR are the following: (1) Modeling languages (e.g., GML and UML) and metamodeling are important in SG development. (2) SG is a significant mechanism for cooperative and participative learning strategies. (3) Virtual Reality technology is widely used in safe virtual environments for mining training. (4) There is a need for methodologies that integrate the specification of cognitive functions with the affective part of the users for SGs suitable for learning environments. Finally, this review highlights critical gaps in current research and underscores the need for more integrative approaches to SG development. Full article

Occupational Health and Safety (OHS) in agriculture is a critical concern worldwide, with self-propelled machinery accidents, particularly tip/roll-overs, being a leading cause of injuries and fatalities. In such a context, while great attention has been paid to machinery safety improvement, a major challenge is the lack of studies addressing the analysis of the work environment to provide farmers with precise information on field slope steepness. This information, merged with an awareness of machinery performance, such as tilt angles, can facilitate farmers in making decisions about machinery operations in hilly and mountainous areas. To address this gap, the Italian Compensation Authority (INAIL) launched a research programme to integrate georeferenced slope data with the tilt angle specifications of common self-propelled machinery, following EN ISO 16231-2:2015 standards. This study presents the first results of this research project, which was focused on vineyards in the alpine region of the Autonomous Province of Trento, where terrestrial LiDAR technology was used to analyze slope steepness. The findings aim to provide practical guidelines for safer machinery operation, benefiting farmers, risk assessors, and manufacturers. By enhancing awareness of tip/roll-over risks and promoting informed decision-making, this research aims to contribute to improving OHS in agriculture, particularly in challenging terrains. Full article

Despite the introduction of preventive safety measures, such as near-miss reporting, to mitigate accidents and minimize fatalities, construction workers are constantly exposed to stressful situations that negatively affect their safety behavior and reporting efficiency. Occupational stress is induced by various factors, with mental stress and auditory stress being common workplace stressors that impact workers on the job site. While previous studies have demonstrated the effect of stressor conditions on workers’ hazard recognition and safety performance, research gaps persist regarding the direct impact of workplace stressors on workers’ stress levels and near-miss recognition performance. This study investigates workers’ near-miss recognition ability through an eye-tracking experiment conducted in a controlled environment under mental and auditory stress conditions. The findings from this study reveal that workplace stressors triggered by mental and auditory stress can adversely affect worker stress levels, safety behavior, and cognitive processing toward near-miss recognition. Visual attention towards near-miss scenarios was reduced by 26% for mental stress conditions and by 46% for auditory stress conditions compared to baseline. The results may potentially open avenues for developing wearable stress prediction and safety intervention models using bio-sensing technology and personalized safety training programs tailored to individuals with low identification abilities. Full article

Given the complexity of the crashes and the increasing interest in public policies related to the reduction in both accidents and fatalities from road crashes, the proposed review of the specialty literature may serve as a starting point for individuals interested in developing studies related to road vehicle accidents, reconstruction methodologies, assessment of vehicles crashworthiness, as well as evaluation of occupants’ behavior in different collision scenarios. Therefore, the present paper aims to offer a comprehensive overview of the specialty literature approaches in terms of road vehicle accidents through an analysis of the reconstruction methods used in the cases of vehicle-to-vehicle or vehicle-to-object crashes, as well as ways in which the crashworthiness of road vehicles is assessed by specialized organizations or individual experts. The addressed topics were summarized from a range of European and global strategies in the field of transportation, reports, testing protocols, as well as scientific research papers published in international databases. The main purpose of the present paper is to serve as a foundational resource for researchers and practitioners seeking to contextualize their work within a global framework. Full article

Small- and medium-sized enterprise (SME) mining firms contribute to Chile’s economy. However, more support is needed to improve decision-making, mainly in a context where it is necessary for mining to operate sustainably. Loading and transportation are essential unit operations in mining. Solution-focused supplier companies are joining the market, making selection more difficult. This study suggests a hierarchical analytical process-based multi-criteria analysis. Among what stands out are its simplicity and clarity. The Analytic Hierarchy Process (AHP), used in several fields, is a flexible multi-criteria analysis system for complex decision-making. Its development used Expert Choice^® software. The results show that the most crucial criterion for selecting loading and transportation equipment suppliers is related to occupational safety and health. The most relevant components are the mortality, accident frequency, and severity rates. Operational indicators are the second most relevant criterion, enabling companies to be more productive and efficient in achieving their objectives. Sensitivity analysis demonstrates that, even with variations in the criterion preferences, the fatality rate remains at the top of the hierarchy, showing the robustness of the model used. Contrary to what might be expected, criteria such as the supplier profile do not stand out among the critical factors for the sector. Full article