Search Results (121)

Industry 4.0 advanced technologies are increasingly used to monitor workers and reduce accident risks to ensure workplace safety. In this paper, we present an on-premise, rule-based safety management system that exploits the fusion of data from an Ultra-Wideband (UWB) Real-Time Locating System (RTLS) and AI-based video analytics to enforce context-aware safety policies. Data fusion from heterogeneous sources is exploited to broaden the set of safety rules that can be enforced and to improve resiliency. Unlike prior work that addresses PPE detection or indoor localization in isolation, the proposed system integrates an UWB-based RTLS with AI-based PPE detection through a rule-based aggregation engine, enabling context-aware safety policies that neither technology can enforce alone. In order to demonstrate the feasibility of the proposed approach and showcase its potential, a proof-of-concept implementation is developed. The implementation is exploited to validate the system, showing sufficient capabilities to process video streams on edge devices and track workers’ positions with sufficient accuracy using a commercial solution. The efficacy of the system is assessed through a set of seven safety rules implemented in a controlled laboratory scenario, showing that the proposed approach enhances situational awareness and robustness, compared with a single-source approach. An extended validation is further employed to confirm practical reliability under more challenging operational conditions, including varying camera perspectives, diverse worker clothing, and real-world outdoor conditions. Full article

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems. Full article

This entry presents the history, geography, business, regulations, and the roles of gig workers, platform/algorithms, and employers, focusing primarily on the USA and the EU. The gig economy is informally referred to also as the fourth industrial revolution or the 1099 economy, emphasising sharing, freelance, or platform work; it is a complex and changing business model and regulatory environment. In practice, the gig economy refers to a tripartite relation between workers, platforms/apps, and employers, leading to a two-sided market, where algorithms match supply and demand for paid labour and clients. It is only recently that the gig economy has started to be conceptualised, and its implications, challenges, and impacts are captured in economic law and society, including the power dynamics related to the interplay between economics, technology, regulation, and communities. Conceptually, the gig economy is important, as small paid work has always been present in society for all types of workers and beneficiaries. This new business model of on-demand work has some perceived advantages, such as freedom of work, under-regulation, efficient use of capital, driving down costs, and improving services. However, there is a dualisation of anti-power between workers and non-employers that may lead to precarious work, less free workers, and shadow corporations that distort the market using game changers like digital management algorithms. Currently, the size of the gig economy comprises 154–435 million gig workers out of the world’s 3.63 bn workers, with a market size of USD 557 bn, and is still expanding. Full article

The construction industry has long been recognized as one of the world’s most hazardous sectors, with safety issues remaining a persistent challenge. To enhance sustainable safety management in this field, this study employs Self-Determination Theory (SDT) to explore the psychological mechanisms underlying construction workers’ safety behaviors. Through structural equation modeling using SPSS 27 and AMOS 28 software on 334 questionnaires, the research reveals that safety awareness and work pressure not only directly influence safety behaviors but also mediate through psychological factors. Notably, social identity significantly moderates the cognitive–behavioral pathway, while autonomous and control motivations moderate the psychological–behavioral relationship. This study breaks through the limitations of traditional safety management that focuses solely on external factors, constructing a multi-level theoretical model that encompasses cognitive, stress, psychological, motivational, and social factors. The research provides a theoretical foundation and practical pathway for construction enterprises to implement people-oriented safety management, contributing to the Sustainable Development Goals centered on the health and safety of employees. Full article

Welding is an essential process across various industries; however, it exposes workers to dangerous fumes, extreme heat and physical stress, which pose considerable health and safety hazards. To tackle these issues, this article introduces the creation of a remote-controlled human–robot welding system aimed at safeguarding workers while ensuring the quality of the welds. The system monitors a welder’s torch movements through a stereoscopic sensor and accurately reproduces them with a robotic arm, facilitating real-time remote welding. Operated by a student, it effectively welded standardized sheet metals in overhead positions while adhering to critical quality standards. The weld geometry met ISO 5817 requirements, tensile strength surpassed the base material specifications, and bending and hardness assessments verified the durability and integrity of the welds. When utilized in hazardous settings, the system showcases its capability to produce high-quality welds while significantly enhancing worker safety, underscoring its potential for real-world industrial applications. Full article

Within the hazardous construction industry, roofers represent one of the most at-risk workforces, with high fatalities and injury rates largely driven by Work-Related Musculoskeletal Disorders (WMSDs). The primary precursor to these disorders is muscle fatigue, yet its objective assessment remains a significant challenge for implementing proactive safety management. To address this gap, this study details the implementation and validation of an AI-driven predictive analytics framework for automated fatigue detection using surface electromyography (sEMG) signals. Data was collected as participants (novice roofers) performed strenuous, simulated roofing tasks involving sustained standing, stooping, and kneeling postures. A key innovation is a data-driven labeling methodology using Weak Monotonicity (WM) trend analysis to automate the generation of objective labels. After a feature selection process yielded seven significant features, an evaluation of standard models confirmed that their classification performance was highly posture-dependent, motivating a more robust, hybrid solution. The framework culminates in a high-performance hybrid machine learning model. This architecture synergistically combines a Transformer–LSTM network for deep feature extraction with an XGBoost classifier. The model outperformed all standalone approaches, achieving over 82% accuracy across all postures with consistently strong fatigue F1-scores (0.77–0.78). The entire framework was validated using a stringent Leave-One-Subject-Out (LOSO) cross-validation protocol to ensure subject-independent generalizability. This research provides a validated component for AI-enhanced safety management systems. Future work should prioritize field validation with professional workers to translate this framework into practical, real-world ergonomic monitoring systems. Full article

With the advent of Industry 4.0, sensor-based human activity recognition has become increasingly vital for improving worker safety, enhancing operational efficiency, and optimizing workflows in Internet-of-Things (IoT) environments. This study introduces a novel deep learning-based framework for construction worker activity recognition, employing a deep residual convolutional neural network (ResNet) architecture integrated with multi-sensor fusion techniques. The proposed system processes data from multiple inertial measurement unit sensors strategically positioned on workers’ bodies to identify and classify construction-related activities accurately. A comprehensive pre-processing pipeline is implemented, incorporating Butterworth filtering for noise suppression, data normalization, and an adaptive sliding window mechanism for temporal segmentation. Experimental validation is conducted using the publicly available VTT-ConIoT dataset, which includes recordings of 16 construction activities performed by 13 participants in a controlled laboratory setting. The results demonstrate that the ResNet-based sensor fusion approach outperforms traditional single-sensor models and other deep learning methods. The system achieves classification accuracies of 97.32% for binary discrimination between recommended and non-recommended activities, 97.14% for categorizing six core task types, and 98.68% for detailed classification across sixteen individual activities. Optimal performance is consistently obtained with a 4-second window size, balancing recognition accuracy with computational efficiency. Although the hand-mounted sensor proved to be the most effective as a standalone unit, multi-sensor configurations delivered significantly higher accuracy, particularly in complex classification tasks. The proposed approach demonstrates strong potential for real-world applications, offering robust performance across diverse working conditions while maintaining computational feasibility for IoT deployment. This work advances the field of innovative construction by presenting a practical solution for real-time worker activity monitoring, which can be seamlessly integrated into existing IoT infrastructures to promote workplace safety, streamline construction processes, and support data-driven management decisions. Full article

New quality productive force is a crucial driver for rural transformation. Exploring the impact of this new quality productive force on rural transformation in Shandong Province and enhancing the positive role of regional new quality productive force are significant in promoting high-quality development in this area. Based on urban panel data from 16 prefecture-level cities in Shandong Province, China, spanning from 2010 to 2022, the levels of new quality productive force and rural transformation in Shandong Province are measured separately and an econometric model is constructed to analyze, in depth, the impact of new quality productive force on rural transformation and its mechanism of action. The results show the following. (1) New quality productive force can significantly increase the level of rural transformation in Shandong Province. (2) The urbanization rate of new quality productive force significantly promotes rural transformation, but increases in the average wage of urban workers and the over-advancement of industrial structure significantly inhibit rural transformation. (3) New quality productive force significantly affects the level of rural transformation, mainly by improving the quality of the population. (4) There is regional heterogeneity in the impact of new quality productive forces on rural transformation in the three economic circles of Shandong Province. New quality productivity force provides new dynamic energy for rural transformation in Shandong Province, which can provide new research perspectives and practical guidance for better rural development in China and the rest of the world. Full article

Mobile scaffolding is essential in construction but presents significant safety risks, particularly falls from height (FFH) due to improper use and insufficient monitoring. While prior research has identified hazards, it often lacks robust, actionable solutions, especially regarding the comprehensive analysis of worker behaviors and the spatial context. This study proposed a computer vision-based safety monitoring system that leverages depth cameras for accurate spatial assessments and incorporates temporal conditions to reduce false alarms. The proposed system extends object detection algorithms with mathematical logic derived from safety rules to classify four key unsafe conditions related to safety helmet use, guardrail and outrigger presence, and worker overcrowding on mobile scaffolds. A diverse dataset from multiple sources enhances the model’s applicability to real-world scenarios, while a status trigger module verifies worker behavior over a 3 s window, minimizing detection errors. The experimental results demonstrate high precision (0.95), recall (0.97), F1-score (0.96), and accuracy (0.95) for safe behaviors, with similarly strong metrics for unsafe behaviors. The qualitative analysis further confirms substantial improvements in worker position detection and safety compliance using 3D data over 2D approaches. These findings highlight the effectiveness of the proposed system in improving mobile scaffolding safety, addressing critical research gaps, and advancing construction industry safety standards. Full article

Ancient fairs in China were designated as commercial zones with fixed stalls that had emerged from commodity exchange demands and socio-cultural factors such as clan systems and gentry intervention, exhibiting dual commercial–communal characteristics. Several ancient fairs in Shenzhen have been retained, including Gongming Ancient Fair, which maintains its original spatial configuration adjacent to industrial zones and urban villages, attracting a high concentration of migrant workers. Survey results show that 85% of Gongming residents demand public space renovations, citing inadequacy of the spaces to support public activities. Given the intrinsic link between public spaces and public activities, fostering their positive interaction is crucial for enhancing urban vitality. However, existing studies predominantly focus on the physical environment and neglect activity-driven optimization perspectives. This study first employed social network analysis (SNA) to construct two networks of Gongming Ancient Fair, using the software Ucinet 6.755, including a public space network based on spatial connectivity and a public activity network based on pedestrian flow. Second, the networks’ structural characteristics were analyzed using seven metrics: node degree, density, betweenness centrality, betweenness centralization, clustering coefficient, average path length, and small-world property. Discrepancies between the networks were quantified through betweenness centrality comparisons, with field surveys and interviews identifying causal factors including seasonal product limitations, spatial constraints, inadequate supporting facilities, and substandard management. Based on the survey data and analytical results, the key renovation nodes were categorized into three types: high-control-capacity nodes, high-expectation nodes, and culturally distinctive nodes. Finally, three optimization strategies are proposed. This study integrates sociological perspectives into ancient fair revitalization, addressing gaps in activity-driven spatial research. Full article

Conveyor belts are critical in various industries, particularly in the barrier eddy current separator systems used in recycling processes. However, hidden issues, such as belt misalignment, excessive heat that can lead to fire hazards, and the presence of sharp or irregularly shaped materials, reduce operational efficiency and pose serious threats to the health and safety of personnel on the production floor. This study presents an intelligent monitoring and protection system for barrier eddy current separator conveyor belts designed to safeguard machinery and human workers simultaneously. In this system, a thermal camera continuously monitors the surface temperature of the conveyor belt, especially in the area above the magnetic drum—where unwanted ferromagnetic materials can lead to abnormal heating and potential fire risks. The system detects temperature anomalies in this critical zone. The early detection of these risks triggers audio–visual alerts and IoT-based warning messages that are sent to technicians, which is vital in preventing fire-related injuries and minimizing emergency response time. Simultaneously, a machine vision module autonomously detects and corrects belt misalignment, eliminating the need for manual intervention and reducing the risk of worker exposure to moving mechanical parts. Additionally, a line-scan camera integrated with the YOLOv11 AI model analyses the shape of materials on the conveyor belt, distinguishing between rounded and sharp-edged objects. This system enhances the accuracy of material separation and reduces the likelihood of injuries caused by the impact or ejection of sharp fragments during maintenance or handling. The YOLOv11n-seg model implemented in this system achieved a segmentation mask precision of 84.8 percent and a recall of 84.5 percent in industry evaluations. Based on this high segmentation accuracy and consistent detection of sharp particles, the system is expected to substantially reduce the frequency of sharp object collisions with the BECS conveyor belt, thereby minimizing mechanical wear and potential safety hazards. By integrating these intelligent capabilities into a compact, cost-effective solution suitable for real-world recycling environments, the proposed system contributes significantly to improving workplace safety and equipment longevity. This project demonstrates how digital transformation and artificial intelligence can play a pivotal role in advancing occupational health and safety in modern industrial production. Full article

Several studies have reported the incidence of fungi and mycotoxins in coffee beans; however, there are few reports related to occupational exposure to these agents at coffee dry milling industries. The aim of this review was to identify and to analyze studies assessing occupational exposure to fungi and mycotoxins in coffee industries. Therefore, a systematic literature search was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and focusing on the assessment of occupational exposure to fungi and mycotoxins in the coffee industry. In these papers, different environmental matrices were considered in evaluating occupational exposure, but the most used matrix was airborne dust (four of the five studies). Airborne fungi were sampled using active (four of the five studies) and passive sampling. Only the most recent of the studies (2022) identified microorganisms by their genera and species, and only two groups of mycotoxins were analyzed in the studies considered, namely, Ochratoxin A and Aflatoxins. None of the studies reported data on both fungi and mycotoxins. The fungal genera identified in these occupational environments included Cladosporium, Paecilomyces, Aspergillus, Penicillium, and other genera. Among the mycotoxins, only aflatoxins and ochratoxin A were investigated. Occupational exposure to these biological agents may lead to several health effects. Fungal spores and fragments can cause respiratory diseases such as asthma, allergic rhinitis, bronchitis, and hypersensitivity pneumonitis. Additionally, the mycotoxins studied—particularly Aflatoxins and Ochratoxin A—are associated with serious toxicological effects. Coexposure to both fungi and mycotoxins may enhance health risks and should be carefully considered in occupational risk assessments. Considering the possible effects related to exposure to fungi and mycotoxins and the number of workers involved in this type of industry in the world, more studies should be developed. This is the first review to systematically consolidate data on occupational exposure to both fungi and mycotoxins specifically within the coffee industry, highlighting existing knowledge gaps and the need for targeted risk assessments in coffee-producing settings. Full article

The choice between domestic and foreign production is one of the most important decisions not only for international business management but also for economic diplomacy and industrial policy. The reality is not a binary choice, but some firms use both. Why do companies maintain labour-intensive production in developed countries in the globalised world? To understand business challenges and strategies, this study examines small and medium-sized enterprises (SMEs) in the garment factory agglomeration in the North (Kenpoku) area of Iwate Prefecture, Japan. The in-depth case study, with a special focus on the six competitiveness factors of Japanese apparel firms, recognises that the ‘Made in Japan’ branding strategy is one of the effective ways to attract Japanese customers. This marketing strategy may motivate some firms to consider international market development. However, most Japanese SME apparel manufacturers play the role of original equipment manufacturer (OEM) for specific domestic market-oriented apparel companies. To meet customers’ strict delivery requirements, our case SMEs are developing multi-skilled workers to cope with high-mix small-lot production and fast delivery simultaneously. This management innovation is essential for building long-term business relationships and trust with corporate apparel buyers and surviving competition from products made in China and other developing countries. Full article

Musculoskeletal injuries comprise a wide range of physical conditions impacting the coordination of bones, muscles, and joints. Estimations suggest that close to one-third of the world’s population will experience a musculoskeletal or non-musculoskeletal injury at some point in their life. Musculoskeletal injuries affect athletes, office workers, industrial workers, older adults, and children every year. Among individuals over the age of 65, musculoskeletal injuries disproportionately affect older women, limiting their ability to maintain an active and professional life or engage in leisure activities during retirement. The field of physical therapy has recently expanded to build an understanding of the complex, non-linear interactions between the gut microbiota and the musculoskeletal system. There is an unexpected connection between the gut microbiota and both the experience of musculoskeletal pain and the healing process following musculoskeletal injuries. Understanding the mechanisms of the microbiota’s influence on these injuries could inform healthcare strategies aimed at prevention and recovery. For patients who suffer from or are at risk of developing musculoskeletal injuries, analyzing the composition of their microbiota plays a crucial role in patient stratification, which can significantly enhance the effectiveness of prevention and treatment strategies. Full article

There has been a growth of collaborative robots in Industry 5.0 due to the research in automation involving human-centric workplace design. It has had a substantial impact on industrial processes; however, physical exertion in human workers is still an issue, requiring solutions that combine technological innovation with human-centric development. By analysing real-world data, machine learning (ML) models can detect physical fatigue. However, sensor-based data collection is frequently used, which is often expensive and constrained. To overcome this gap, synthetic data generation (SDG) uses methods such as tabular generative adversarial networks (GANs) to produce statistically realistic datasets that improve machine learning model training while providing scalability and cost-effectiveness. This study presents an innovative approach utilising conditional GAN with auxiliary conditioning to generate synthetic datasets with essential features for detecting human physical fatigue in industrial scenarios. This approach allows us to enhance the SDG process by effectively handling the heterogeneous and imbalanced nature of human fatigue data, which includes tabular, categorical, and time-series data points. These generated datasets will be used to train specialised ML models, such as ensemble models, to learn from the original dataset from the extracted feature and then identify signs of physical fatigue. The trained ML model will undergo rigorous testing using authentic, real-world data to evaluate its sensitivity and specificity in recognising how closely generated data match with actual human physical fatigue within industrial settings. This research aims to provide researchers with an innovative method to tackle data-driven ML challenges of data scarcity and further enhance ML technology’s efficiency through training on SD. This study not only provides an approach to create complex realistic datasets but also helps in bridging the gap of Industry 5.0 data challenges for the purpose of innovations and worker well-being by improving detection capabilities. Full article