Search Results (108)

The proper wearing of safety helmets is critical for worker safety in high-risk construction environments, with the fastening of the chin strap serving as a key indicator of correct usage. However, existing detection methods primarily focus on identifying helmet presence, neglecting the crucial assessment of chin strap compliance. This paper proposes an intelligent detection approach that integrates YOLOv8 object detection, instance segmentation, and skin tone recognition to evaluate chin strap wearing status. The system first employs YOLOv8 to detect workers and helmets, filtering out non-wearers before performing facial and neck region segmentation, thereby concentrating computational resources on compliance verification. To address challenges in distinguishing chin straps from similar skin tones under complex lighting conditions, the method incorporates illumination compensation and YCbCr-based skin segmentation. Finally, strap status is determined through morphological operations and contour analysis, with visual annotation of the detection results. This study utilizes a dataset comprising 2000 safety helmet images, which was partitioned into training, validation, and test sets in an 8:1:1 ratio for model training and evaluation. The experimental results demonstrate that the proposed method achieves an accuracy of 96% in detecting chin strap status, exhibits robust performance across diverse construction site conditions, and holds significant practical value and application potential. Full article

Head injuries remain one of the major health concerns in contact sports such as boxing. Despite the widespread use of protective gloves and helmets, their biomechanical effectiveness in mitigating head acceleration and reducing brain injury risk remains uncertain. This study aims to biomechanically assess available boxing equipment solutions and identify the brain–skull system’s response to physical forces from a boxing punch. A dedicated experimental setup was developed using mini triaxial accelerometers and a high-speed camera to measure head accelerations in a Primus unbreakable dummy. Tests were performed using gloves of different masses (0 oz, 10 oz, and 16 oz) and three head protection configurations: no helmet, rugby helmet, and boxing helmet. The resultant accelerations were analyzed and compared across test conditions. Peak wrist accelerations ranged from 195.00 to 271.77 m/s², while head accelerations did not exceed biomechanical injury thresholds. The boxing helmet, composed of multilayer polyurethane foam, did not consistently decrease acceleration; in some cases, it produced higher overloads due to increased head mass and moment of inertia. A rugby helmet made of open-cell EVA (ethylene vinyl acetate) foam with lower density exhibited more favorable energy-dissipation characteristics under low-impact conditions. Glove mass also influenced acceleration differently between male and female participants, likely due to variations in punch velocity and force generation. This work is a pilot study using two trained adult volunteers to validate the combined IMU–video measurement framework. The results serve as hypothesis-generating mechanistic observations rather than population-level effect estimates. Protective effectiveness in boxing depends on a complex interaction between material properties, geometry, and user biomechanics. Optimal equipment design should balance energy absorption and mass to minimize both linear and rotational accelerations. Future studies should integrate advanced material modeling and finite element simulations to support the development of adaptive, lightweight protective systems. Full article

The high rate of bike commuting around the globe has greatly transformed the mode of transportation in cities, but the high speeds of motorized cycling have contributed to a high rate of serious road trauma. Although conventional helmets offer necessary passive structural protection, they do not consider the most important aspect of the emergency response, which is the Golden Hour the time frame during which medical intervention can have the most significant impact. This paper is a development and validation of an autonomous, low-cost smart helmet architecture that is programmed to operate in real-time to detect accidents and autonomously inform the operator of accidents. The system is built up of an ESP32 microcontroller with a multi-modal sensor package, which comprises an inertial measurement unit (IMU), force-impact sensors, and MQ-3 alcohol sensors to conduct proactive safety screening. To overcome the single threshold limitation of unreliable systems, a time-windowed sensor-fusion algorithm was applied in order to distinguish between normal riding dynamics and bona fide collisions. This reasoning involves concurrent cues of high-G inertial rotations and physical impacting features over a time window of 500 ms to reduce spurious activations. The architecture of the system is completely self-sufficient and employs an in-built GPS-GSM module to send the geographical location through SMS without the need to have a smartphone connection. The prototype was also put through 150 experimental tests, with some conducted in laboratories, and real-world running tests in diverse terrains. The findings reveal an accuracy in detection of 93.7, a false positive rate (FPR) of 2.6 and a mean emergency alert latency of 2.8 s. In addition, it was found that structural integrity was confirmed at ECE 22.05 impact conditions using Finite Element Analysis (FEA), with a safety factor of 1.38. These quantitative results mean that the proposed system is an effective way to address a cultural shift between passive structural protection and active rescue intervention as a statistical and computationally efficient safety measure of modern micro-mobility. Full article

Construction site safety remains a critical global challenge, demanding urgent attention. Existing surveillance systems struggle to balance multi-object detection accuracy, real-time efficiency, and environmental robustness under strict edge constraints. This paper presents MMDet-Edge, an edge-optimized unified detection framework that addresses these competing demands via three synergistic innovations. First, an adaptive feature fusion architecture with a learnable spatial–channel attention mechanism resolves cross-scale conflicts, boosting small-object average precision (AP) by 9.3%. Second, a hardware-conscious neural architecture search (HC-NAS) strategy co-optimizes sparsity patterns and quantization sensitivity, achieving a state-of-the-art performance of 89.4% mAP@0.5 at only 1.8 W power consumption—surpassing contemporary edge detectors by 6.3% mAP under equivalent power budgets. Third, by incorporating OSHA fatality statistics into a novel risk-weighted evaluation paradigm, we reduce high-consequence false negatives by 34%. Comprehensive evaluations on a purpose-built benchmark and cross-dataset tests demonstrate MMDet-Edge’s superiority. It outperforms a wide range of state-of-the-art models. Validated across three active construction sites, the system enables real-time detection of five safety-critical targets (personnel, helmets, flames, smoke, vests) under extreme conditions, including >60% occlusion and >100 lux illumination variance. Our field deployments demonstrated a 22% reduction in safety incidents compared to conventional systems, establishing a new architectural paradigm for safety-critical edge AI through principled hardware–algorithm co-design. Full article

This research examines the risk factors that influence injury severity in individual motorcycle accidents, utilising a dataset of 5253 incidents. Five machine learning algorithms—multinomial logistic regression, classification trees, random forests, XGBoost, and neural networks—were used to classify the results into three groups: Death (13.48%), Injury (80.14%), and No injury (6.38%). In all models, passenger presence was the most important predictor of injury. Motorcycle accidents involving passengers do not always have more serious consequences for several overlapping reasons. On the one hand, a motorcycle with a passenger has a significantly higher mass, which increases the braking distance and kinetic energy at the moment of collision, hindering quick defensive manoeuvres, cornering, and reactions to sudden hazards. Often, the rider also refrains from sudden movements to prevent the passenger from losing their balance. In the case of single-rider motorcycle accidents on roadways, approximately 5% of those involved with a passenger were fatalities, while approximately 48% were uninjured; in the case of those without a passenger, no one was uninjured. It follows from the above that the presence of a passenger increases the rider’s sense of responsibility. Other factors that significantly increased risk were single-lane carriageways, vehicle overturning, contaminated road surfaces, and collisions with complex objects, e.g., like trees. The multinomial logistic regression model had an overall accuracy of 69.2% on the test set. The Recurrent Neural Network achieved the best overall accuracy of 79.56%. Balanced accuracy, as the average between sensitivity and specificity of the RNN model for the “death” class was 68.15%, for the “injury” class—72.6%, and for the “no injury” class—96.61%. The Area Under the ROC Curve of the Recurrent Neural Networks model for “no injury” was 0.97, indicating it was very good at distinguishing between this class and the other classes. Even though it was easy to tell which cases did not involve injuries, it was still hard to tell the difference between fatal and non-fatal injuries in all models. The results support interventions tailored to specific situations, such as improved road lighting and speed control in rural areas, as well as helmet enforcement and safety measures at intersections in cities. Full article

This study presents the development and mechanical characterization of a full-scale helmet manufactured from a polyester matrix composite reinforced with woven jute fabric using vacuum infusion. Laminates with two and four reinforcement layers were produced and assembled using four joining configurations: seamless, stitched, bonded, and hybrid (bonded + stitched). Tensile tests were performed according to ASTM D3039, while frontal and lateral compression tests followed ABNT NBR 7471, aiming to evaluate the influence of laminate thickness and joining strategy on mechanical performance. In tension, the seamless configuration reached maximum loads of 0.80 kN (two layers) and 1.60 kN (four layers), while the hybrid configuration achieved 0.79 kN and 1.43 kN, respectively. Stitched and bonded joints showed lower strength. Under compression, increasing the laminate thickness from two to four layers reduced frontal elongation from 15.09 mm to 9.97 mm and lateral elongation from 13.73 mm to 7.24 mm, corresponding to stiffness gains of 50.3% and 87.3%, respectively. Statistical analysis (ANOVA/Tukey, α = 0.05) confirmed significant effects of thickness and joint configuration. Although vacuum infusion is a well-established process, the novelty of this work lies in its application to a full-scale natural-fiber helmet, combined with a systematic evaluation of joining strategies and a direct correlation between standardized tensile behavior and structural compression performance. The four-layer hybrid laminate exhibited the best balance between strength, stiffness, and deformation capacity. Full article

The construction industry relies on the Internet of Things (IoT) and wearable technologies to enhance workplace safety. This research investigates the use of IoT and wearable technology among Saudi Arabian construction sector employees, analyzing their implementation difficulties and the factors contributing to successful implementation. A structured questionnaire was distributed to 567 construction professionals across different roles and projects. Frequency analysis was used to study adoption patterns, chi-square tests to study demographic factors, and principal component analysis for exploratory factor analysis to discover hidden adoption factors. The findings show that smart safety vests and helmets receive the highest level of recognition. On the other hand, advanced monitoring systems, including fatigue and environmental sensors, are not used enough. Group differences in device adoption were investigated in terms of years of experience, academic qualification, job role, and project budget. The findings from factor analysis show that three main factors determine adoption rates, which include (1) safety and operational effectiveness, (2) worker acceptance and support structures, and (3) technical and adoption barriers. A data-driven system is created to help policymakers and industry leaders accelerate construction safety digitalization efforts. Full article

Background: New York State, like many other states, experienced a significant increase in road traffic deaths during the COVID-19 pandemic that is not fully understood. Our earlier work using the Safe System framework suggests a shift in the distribution of vehicle types that may have contributed to this phenomenon. Methods: To further investigate this, variables from the Fatality Analysis Reporting System (FARS) were mapped onto the Safe System framework and used to examine factors associated with motorized two- and three-wheeled vehicle deaths. Two time periods were examined: pre-pandemic (1 April 2017–31 December 2019, n = 428) and the COVID-19 pandemic era (1 April 2020–31 December 2022, n = 600). A buffer pandemic transition period (1 January 2020–31 March 2020) was excluded. Percent difference, chi-square tests, and multivariable logistic regression (OR, 95% CI) were used. Results: Compared to pre-COVID-19, pandemic-period motorized two-wheeled deaths were 40.2% higher, helmet wearing lower (80.2% vs. 90.6%, p < 0.0001), urban roadway deaths higher (76.7% vs. 64.0%, p < 0.0001), and fully licensed drivers lower (78.4% vs. 89.9%, p < 0.0001), with unlicensed drivers doubling between the two periods (8.7% to 17.6%, p < 0.0001). Deaths associated with mopeds/motor scooters/minibikes increased 361.5% between study periods, from 3% to 10% of motorized two-wheeled deaths. Adjusted multivariable risk factors for pandemic-period death were age 30–39 years (1.601, 1.155–2.311), being unhelmeted (3.191, 2.109–4.968), being in an urban area (1.898, 1.425–2.533), being unlicensed (1.968, 1.228–33.216) and riding an off-road motorcycle (3.753. 1.391–13.063), moped or motor scooter/minibike (3.540, 1.971–6.842). Conclusions: Total mortality was higher in the COVID-19–era timeframe, with the increase differing significantly by vehicle type, helmet use, licensure status, and urbanization. Due to the increase in motorized two-wheeled vehicles, they should be incorporated into surveillance systems and injury prevention strategies. Full article

Despite preventive measures, the construction industry continues to exhibit high accident rates. In response, visual detection system was developed to support safety management on construction sites and promote sustainable working environments. The solution integrates the YOLOv8 algorithm with asynchronous video processing, incident registration, an open API, and a web-based interface. The system detects the absence of safety helmets (NHD) and worker falls (FD). Its low hardware requirements make it suitable for small and medium-sized construction enterprises, contributing to resource efficiency and digital transformation in line with sustainable development goals. This study advances practice by providing an integrated, low-resource solution that unites multi-hazard detection, event documentation, and system interoperability, addressing a key gap in existing research and implementations. The contribution includes an operational architecture proven to run in real time, addressing a gap between model-centred research and deployable, OHS applications. The system was validated using two independent test datasets, each comprising 100 images: one for NHD and one for FD. For NHD, the system achieved a precision of 0.93, an accuracy of 0.88, and an F1-score of 0.79. For FD, a precision of 1.00, though with a limited recall of 0.45. The results demonstrate the system’s potential for sustainable construction site safety monitoring. Full article

Objectives: The aim of this study was to determine the epidemiological characteristics of bicycling-related maxillofacial fractures in a defined population and to identify factors contributing to these injuries. Methods: An 8-year cohort study was carried out, including all patients presenting with bicycling-related maxillofacial fractures at a tertiary care center from 2017 through 2024. Data recorded for each patient included age, gender, date and cause of injury, contributing factors, type of facial fractures, other injuries, hospital stay, and helmet use. Statistical analysis was performed. Continuous variables were assessed for normality (Shapiro–Wilk test) and compared using the Mann–Whitney test. Categorical variables were analyzed with chi-square tests. A p-value ≤ 0.05 was considered statistically significant. Results: Out of 899 cycling accident patients seeking medical treatment, 122 (13%) sustained facial fractures, accounting for 4% of all facial fracture cases in our department during the study period. In our cohort, the male–female ratio was 2.6:1, and the mean age was 29.5 years (SD 12.8, range 13–77). Collision with another object/vehicle was the most common cause (64%), followed by isolated falls (36%). A total of 135 facial fractures were recorded (some patients had multiple fractures). Mandibular fractures were most frequent (49% of patients), followed by zygomatic (32%), orbital (13%), nasal (7%), maxillary (2%) and frontal (2%) fractures. Among mandibular injuries, condylar fractures were the most common subtype (63%). Dental injuries were found in 27% of patients. The most common dental trauma was tooth fracture (43% of those with dental injuries), followed by tooth luxation (32%) and tooth avulsion (25%). In 80% of cases involving dental injuries, the upper anterior teeth were involved. Concomitant injuries were present in 20% of patients, most often orthopedic limb injuries. Only 27% of patients reported always wearing a helmet, whereas 43% reported never having worn one. Conclusions: Bicycling-related facial injuries are a noteworthy subset of facial trauma. Missed or delayed diagnosis can lead to lasting deformities and functional issues. Preventive strategies—especially promoting helmet use and improving helmet design—along with broader safety measures are important to reduce the incidence and severity of these injuries. Full article

Although computer vision methods have advanced in construction helmet detection in recent years, their performance heavily depends on large-scale, class-balanced, and diverse annotated datasets. To address the high cost and labor-intensive nature of traditional data collection and annotation, this study introduces a novel helmet detection dataset named AIGC-HWD (Artificial Intelligence-Generated Content–Helmet Wearing Detection), automatically generated using generative AI tools. The dataset contains five categories of labels, supporting both helmet-wearing detection and color classification tasks. We evaluate the standalone performance of AIGC-HWD, as well as its augmentation effect when combined with the real-world dataset GDUT-HWD, using multiple algorithms, including YOLO v8, YOLO v10, YOLO 11, YOLO v11-MobileNet v4, YOLO v13, Faster R-CNN, and RT-DETR. Experimental results show that models trained solely on AIGC-generated images can achieve mAP@50 scores exceeding 0.7 and 0.8 on real-world images in two separate tests, demonstrating a certain level of generalization. When used for data augmentation alongside real-world images, the performance improves to varying degrees—by approximately 1% on the YOLO series, and by over 10% on the two-stage algorithm Faster R-CNN. These findings validate the potential of generative AI images for safety monitoring in construction scenarios and provide new insights into the integration of synthetic and real-world data. Full article

On construction sites, it is crucial and and in most cases mandatory to wear safety equipment such as helmets, safety shoes, vests, and belts. The most important of these is the helmet, as it protects against head injuries and can also serve as a marker for detecting and tracking workers, since a helmet is typically visible to cameras on construction sites. Checking helmet usage, however, is a labor-intensive and time-consuming process. A lot of work has been conducted on detecting and tracking people. Some studies have involved hardware-based systems that require batteries and are often perceived as intrusive by workers, while others have focused on vision-based methods. The aim of this work is not only to detect workers and helmets, but also to identify workers through labeled helmets using symbol detection methods. Person and helmet detection tasks were handled by training existing datasets and gained accurate results. For symbol detection, 14 different shapes were selected and put on helmets in a triple format side by side. A total of 11,243 images have been annotated. YOLOv5 and YOLOv8 were used to train the dataset and obtain models. The results show that both methods achieved high precision and recall. However, YOLOv5 slightly outperformed YOLOv8 in real-time identification tests, correctly detecting the helmet symbols. A testing dataset containing different distances was generated in order to measure accuracy by distance. According to the results, accurate identification was achieved at distances of up to 10 meters. Also, a location-based symbol-ordering algorithm is proposed. Since symbol detection does not follow any order and works with confidence values in the inference mode, a left to right approach is followed. Full article

Background: Glioblastoma (GBM) resists current therapies due to its rapid proliferation, diffuse invasion, and heterogeneous cell populations. We previously showed that a single cold atmospheric plasma discharge tube (DT) reduces GBM viability via broad-spectrum electromagnetic (EM) emissions. Here, we tested whether two DTs arranged in a helmet configuration could generate overlapping EM fields to amplify the anti-tumor effects without thermal injury. Methods: The physical outputs of the single- and dual-DT setups were characterized by infrared thermography, broadband EM field probes, and oscilloscope analysis. Human U87-MG cells were exposed under the single or dual configurations. The viability was quantified with WST-8 assays mapped across 96-well plates; the intracellular reactive oxygen species (ROS), membrane integrity, apoptosis, and mitochondrial potential were assessed by multiparametric flow cytometry. Our additivity models compared the predicted versus observed dual-DT cytotoxicity. Results: The dual-DT operation produced constructive EM interference, elevating electric and magnetic field amplitudes over a broader area than either tube alone, while temperatures remained <39 °C. The single-DT exposure lowered the cell viability by ~40%; the dual-DT treatment reduced the viability by ~60%, exceeding the additive predictions. The regions of greatest cytotoxicity co-localized with the zones of highest EM field overlap. The dual-DT exposure doubled the intracellular ROS compared with single-DT and Annexin V positivity, confirming oxidative stress-driven cell death. The out-of-phase operation of the discharge tubes enabled the localized control of the treatment regions, which can guide future treatment planning. Conclusions: Two synchronously operated plasma discharge tubes synergistically enhanced GBM cell killing through non-thermal mechanisms that coupled intensified overlapping EM fields with elevated oxidative stress. This positions modular multi-DT arrays as a potential non-invasive adjunct or alternative to existing electric-field-based therapies for glioblastoma. Full article

Carbon–Kevlar hybrid composites are being increasingly recognized as suitable materials for aerospace, automotive, and construction applications due to their unique combination of strength, toughness, and safety. Prior to their use, extensive testing and validation are essential to ensure that these composites meet the specific safety and performance standards required by each industry. In this study, the mechanical performance and behavior of five different types of Carbon–Kevlar hybrid composites were investigated. In addition to microstructural investigations, mechanical tests were also carried out, including tensile, bending, impact, and micro-hardness tests. The investigated composites were Carbon–Kevlar hybrids without orientation, with a symmetrical orientation, and with the addition of TiO₂ nanoparticles at weight percentages of 3%, 4%, and 5%. The results showed that the mechanical properties of these composites could be significantly influenced by different fiber orientations and the addition of TiO₂ nanoparticles. In particular, the addition of TiO₂ nanoparticles increased the tensile strength, hardness, toughness, and breaking strength. Of the composites tested, the composite reinforced with 5% TiO₂ nanoparticles exhibited the highest mechanical performance, with a 79.8 Shore D hardness, 406 MPa tensile strength, 398 N/mm² flexural strength, and 10.1 J impact energy. These results indicate that Carbon–Kevlar hybrid composites reinforced with TiO₂ nanoparticles have excellent mechanical properties that make them highly suitable for armor plating, helmets, and vehicle armoring in particular and a wide range of other industrial applications in general. Full article

Brain activity plays a fundamental role in science and technology, particularly in the advancement of cognitive process therapies. Gaining a deeper understanding of brain function can contribute to the development of more effective therapeutic strategies aimed at enhancing cognitive performance and mental well-being. Advances in technological innovation in the health sector have allowed the creation of portable wireless electroencephalogram (EEG) devices, which make recordings in contexts outside the laboratory or clinical area. This work aims to design, manufacture, and acquire data on the Chameleon-1 helmet used by young and adult people people in different health states. The data acquisition of the EEG signals is carried out using two electrodes positioned at points $F_3$ and $F_4$, which are placed with the international 10–20 system. Tests were performed on several university participants. The recorded results show reliable, precise, and stable data in each patient with an average concentration of 91%. Excellent results were obtained from patients with different health conditions. In these records, the efficiency and robustness of the Chameleon-1 helmet were verified in adapting to any skull and with good data precision without noise alteration. Full article