Search Results (386)

In the footwear industry, occupational risks are significant, and work accidents are frequent. Professionals in the field prepare documents and reports about these accidents, but the need for more time and resources limits learning based on past incidents. Machine learning (ML) and deep learning (DL) methods have been applied to analyze data from these documents, identifying accident patterns and classifying the damage’s severity. However, evaluating the performance of these methods in different economic sectors is crucial. This study examined neural and non-neural methods for classifying the severity of workplace accidents in the footwear industry complex. The random forest (RF) and extreme gradient boosting (XGBoost) methods were the most effective non-neural methods. The neural methods 1D convolutional neural networks (1D-CNN) and bidirectional long short-term memory (Bi-LSTM) showed superior performance, with parameters above 98% and 99%, respectively, although with a longer training time. It is concluded that using these methods is viable for classifying accidents in the footwear industry. The methods can classify new accidents and simulate scenarios, demonstrating their adaptability and reliability in different economic sectors for accident prevention. Full article

Fatal and non-fatal accidents and suicides at work pose a substantial threat to workers’ physical and psychological safety, particularly within the construction industry. Managing these incidents is an essential component of workplace health and safety (WHS). Additionally, a formal program to support workers and provide feedback to improve the existing WHS management system is increasingly important. However, knowledge of the factors contributing to an effective critical incident (CI) or postvention response is limited by the lack of published evidence-based interventions, especially for occupations and industries that are exposed to higher rates of fatal and non-fatal injuries due to accidents and suicides. In addition, broader concerns surrounding the effectiveness of conventional critical incident programs highlight the need to develop innovative and evidence-based critical intervention and postvention responses addressing acute stress symptoms arising from exposure. This research outlines the development of the MATES Respond Training Program, a component of the WHO-endorsed MATES in Construction program, which is a charity charged with the task of improving mental health and suicide prevention in the construction industry in Australia and New Zealand. Development of the MATES Respond Training Program was informed by the following three key sources: a rapid literature review, interviews with 11 workers trained in peer support suicide prevention interventions, and an analysis of site notes on 193 critical incident and postvention construction site attendances by MATES in Construction field officers, case managers and social workers. Insights and recommendations obtained from these sources, along with the resulting program, are discussed in this paper. Full article

Advanced Driver Assistance Systems, such as Forward Collision Warning and Lane Departure Warning, play a crucial role in accident prevention by alerting drivers to potential hazards. With the advent of fully autonomous driving technology that requires no driver input, there is now a greater emphasis on monitoring the state of vehicle occupants. This is particularly important because, in emergency situations where control must suddenly be transferred to an unprepared occupant, the risk of accidents increases significantly. To mitigate this risk, new monitoring technologies are being developed to analyze driver behavior and detect states of inattention or drowsiness. In response to the emerging demands of driver monitoring technology, we have developed the Customized Driver Inattention Detection Model (CDIDM). This model employs video analysis and statistical techniques to accurately and rapidly classify information on drivers’ gazes. The CDIDM framework defines the components of inattentive or drowsy driving based on the Driver Monitoring System (DMS) safety standards set by the European New Car Assessment Programme (EuroNCAP). By defining six driving behavior-related scenarios, we have improved the accuracy of driver inattention assessment. The CDIDM estimates the driver’s gaze while simultaneously analyzing data in real-time. To minimize computational resource usage, this model incorporates a series of preprocessing steps that facilitate efficient time series data analysis, utilizing techniques such as DTW Barycenter Averaging (DBA) and K-means clustering. This results in a robust driver attention monitoring model based on time series classification. Full article

One of the most important values in human life is health and safety. This is reflected in the Sustainable Development Goals (SDG3, SDG8), which were presented by the United Nations in 2015. These goals emphasize the importance of ensuring healthy lives, promoting well-being for all, and fostering inclusive and sustainable economic growth through decent work and safe working conditions. Taking into account the importance of issues related to safety, including occupational safety and human health, research was carried out to examine the impact of the development of a safety culture during employees’ work on improving public health. In order to conduct the research, a survey questionnaire was developed, consisting of 20 questions relating to 10 attributes of safety culture. The research surveys were carried out among 363 employees in a production enterprise, and an interview questionnaire was administered to 3 employees of the Occupational Health and Safety Service of this enterprise. The results revealed that the enterprise’s employees rate the level of safety culture very highly. Along with the improvement of working conditions in the enterprise, the level of safety increases, the number of accidents at work decreases, and thus the protection of employee health is higher. Therefore, a high level of safety culture is correlated with a high value attached to the health and life of employees, and this, in turn, affects public health. Moreover, fostering a strong safety culture aligns with the principles of sustainable development, contributing to long-term societal well-being and aligning with global goals for responsible and ethical growth. Full article

The number of migrant workers in Saudi Arabia (SA) has gradually increased, particularly in the construction industry, where migrant workers make up 89% of the workforce. Migrant workers frequently experience exposure to dangerous working conditions and increased risk for occupational injury and hazards due to the work they typically perform. Despite this, there is a lack of comprehensive studies comparing occupational accidents and injuries between migrant and domestic workers. To address this challenge, this study explores the differences between migrant and domestic workers’ injuries and occupational accident rates in SA’s construction industry. Data were analyzed from reported accidents and injuries obtained from the General Organisation for Social Insurance (GOSI) between 2014 and 2019. Chi-square test was used to examine the associations of occupational accidents and injuries among migrant and domestic workers. Migrant workers experienced higher incidences of falls, strikes, collisions, abrasions (wounds caused by scraping), bodily reactions (e.g., chemical reactions), and car accidents compared to domestic workers. Furthermore, migrant workers aged 30–39 and domestic workers aged 20–29 experienced more severe injuries and higher seasonal mortality rates during the six-year period examined (2014–2019). In addition, domestic workers achieved a higher proportion of full recovery across all types of accidents, except for transport and car accidents related to construction. The findings emphasize the need for ongoing safety education, training, and improved safety measures to protect the health and safety of construction workers, especially migrant workers. Full article

Construction accidents are a major contributor to occupational fatalities. To tackle this issue, improved monitoring for hazard elimination is crucial. By introducing a deep-learning image captioning system, we identified hazards via closed-circuit television in construction sites. By leveraging Inception-v3 for feature extraction and the gated recurrent unit for caption generation, real-time hazard monitoring was enabled. Bilingual evaluation understudy scores were determined for continuous and effective hazard detection, helping construction managers enhance safety. Full article

Decompression sickness (DCS) with neurological disorders is the leading cause of major diving accidents treated in hyperbaric chambers. Exposure to high levels of CO₂ during diving is a safety concern for occupational groups at risk of DCS. However, the effects of prior exposure to CO₂ have never been evaluated. The purpose of this study was to evaluate the effect of CO₂ breathing prior to a provocative dive on the occurrence of DCS in mice. Fifty mice were exposed to a maximum CO₂ concentration of 70 hPa, i.e., 7% at atmospheric pressure, for one hour at atmospheric pressure. Another 50 mice breathing air under similar conditions served as controls. In the AIR group (control), 22 out of 50 mice showed post-dive symptoms compared to 44 out of 50 in the CO₂ group (p < 0.001). We found that CO₂ breathing is associated with a decrease in body temperature in mice and that CO₂ exposure dramatically increases the incidence of DCS (p < 0.001). More unexpectedly, it appears that the lower temperature of the animals even before exposure to the accident-prone protocol leads to an unfavorable prognosis (p = 0.046). This study also suggests that the composition of the microbiota may influence thermogenesis and thus accidentology. Depending on prior exposure, some of the bacterial genera identified in this work could be perceived as beneficial or pathogenic. Full article

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments. Full article

The construction sector is notorious for its high rate of fatalities globally. Previous research has established that near-miss incidents act as precursors to accidents. This study aims to identify research gaps in the literature on near-miss events in construction and to define potential directions for future research. The Scopus database serves as the knowledge source for this study. To identify publications on near-miss events, the search field “Article Title, Abstract, Keywords” was utilized with the keywords “construction” and “near miss”. The main research themes were defined based on keyword mapping performed using VOSviewer. Selected publications were assessed for their alignment with the defined research theme. A statistical analysis of the publications and the co-occurrence of keywords was conducted. The authors of the identified publications primarily used statistical analyses, artificial intelligence, employee monitoring, tracking systems, and building information modelling in their research. The conclusions from the literature review indicate a need for further research focused on developing effective predictive models for workplace accidents based on knowledge of near-miss events. This will contribute to a better understanding of the mechanisms leading to accidents and their prevention, ultimately resulting in a significant reduction in accidents in the construction sector. Full article

Background: In recent years, there has been a growing use of technological advancements to enhance the rehabilitation of individuals who have suffered from cerebrovascular accidents. Virtual reality rehabilitation programs enable patients to engage in a customized therapy program while interacting with a computer-generated environment. Therefore, our goal was to investigate the effectiveness of virtual reality in occupational therapy for people’s rehabilitation after a cerebrovascular accident. Methods: We systematically searched databases (Pubmed/Medline, Scopus, Web of Science, and Science Direct) for randomized controlled trials published within the last 10 years. Studies involving adult stroke survivors undergoing virtual reality-based interventions aimed at improving upper-extremity motor function were included. The quality assessment followed PRISMA guidelines, with the risk of bias assessed using the Cochrane tool (version 6.4) and methodological quality evaluated using GRADEpro. Results: We selected sixteen studies that met the main criteria for the implementation of virtual reality technology. The interventions described in the articles focused mainly on the upper extremities and their fine motor skills. Conclusions: When used in conventional treatments to improve people’s motor and cognitive functions after a cerebrovascular accident, virtual reality emerges as a beneficial tool. Additionally, virtual reality encourages adherence to the interventional process of rehabilitation through occupational therapy. Full article

This study examines the habits and attitudes of construction sector employees in Türkiye regarding the use of personal protective equipment (PPE). A comprehensive face-to-face survey was conducted with 398 randomly selected employees from small- and medium-sized construction companies across various regions of Türkiye. The survey aimed to identify the frequency of PPE usage and the factors influencing its use and to provide actionable suggestions for improving PPE compliance. The results indicate that while a majority of workers recognize the importance of PPE, actual usage rates are inconsistent. Key factors affecting PPE use include the ergonomics and comfort of the equipment as well as worker perceptions and attitudes. Notably, the study found that the discomfort associated with PPE and the belief that it slows down work pace are significant barriers to its regular use. Additionally, the research highlights the need for a robust punishment–reward system to enforce PPE compliance and suggests that more stringent legislative measures are required to enhance occupational health and safety (OHS) standards in the construction industry. This study contributes to the existing body of knowledge by providing a detailed analysis of PPE usage in the Turkish construction sector and offering practical recommendations for policymakers and industry stakeholders. Implementing these suggestions can lead to improved worker safety and reduced occupational accidents. Full article

The statistics on work-related accidents published by the responsible organizations reveal that the average rate of work accidents within the construction sector is more than double that in other industrial sectors. This serious problem has been analyzed by numerous international organizations and institutes dedicated to occupational safety, health and welfare. Therefore, in this article, some results of a research project that aims to reduce workplace accidents through the standardization of safe work processes and procedures in construction sites are summarized. The proposed methodology consisted of the analysis of national and international bibliographies to analyze the different annual variations in the accident rate, allowing a common pattern to be located, as well as its association with the work processes carried out in construction projects to standardize each of the processes which are present in the execution and life phases of the building. It is possible to conclude that the accident rates can be reduced and/or eliminated with the application of each of the processes thanks to the obtained results. Full article

When analyzing occupational safety factors, a human factor is associated with 80–90% of incidents and accidents that occur. Controlling this factor is essential when it comes to creating healthy and safe organizations. Personality traits have shown great relevance when understanding the behavior of safety or self-protection in the workplace, affecting the motivation of subjects and, therefore, their behavior. The objective of this study is to analyze the influence of the extraversion trait on compliance with safety rules and behaviors, while evaluating the mediating effect of motivation for safety in health professionals. The sample is composed of 183 nurses and wardens of a hospital in Spain. The obtained results confirm the hypotheses raised, validating the negative influences of extraversion on motivation for security and compliance with norms, behaviors, and security, while verifying the effect of the total mediation of motivation for security in the influence of extraversion on security compliance. This confirms the effect of this personality trait on employees’ safety behaviors, and the possibility of reducing this influence by controlling personal motivations with interventions from the organization. Full article

Occupational accidents are a serious public health issue. In this retrospective observational study, we examined all injuries involving healthcare workers of the Abruzzo Local Health Authority No. 1 (Italy) during the three years 2019–2021. Data were collected by tracing the injury reports filed by the emergency service during the workers’ admission and analyzing the cause, type, distribution by sex, and geographical district to which they belonged, taking into account the period before and during the COVID-19 pandemic outbreak. Most injuries (45.7%) were reported in the Marsica area; the most common cause was commuting accidents (10.7%). Assaults were more common among men (8.6%), while commuting accidents were more common among women (11.8%). In 36% of cases, the upper limbs were affected. The most common type of injury was contusions (22.2%). When the frequency of reports was compared between the pre-pandemic and pandemic periods, there was an increase in injuries in healthcare assistants (p = 0.052), while the percentage of injuries in administrative (p = 0.056) and other categories decreased (p = 0.002). This analysis allows us to identify points of interest relating to the Marsica area, to specific types of accidents, such as aggression and commuting accidents, and to specific duties. Full article