Search Results (288)

With the rapid advancement of industrial intelligence, ensuring occupational safety has become an increasingly critical concern. Among the essential personal protective equipment (PPE), safety helmets play a vital role in preventing head injuries. There is a growing demand for real-time detection of helmet chinstrap wearing status during industrial operations. However, existing detection methods often encounter limitations such as user discomfort or potential privacy invasion. To overcome these challenges, this study proposes a non-intrusive approach for detecting the wearing state of helmet chinstraps, inspired by the mechanosensory hair arrays found on spider legs. The proposed method utilizes multiple MEMS inertial sensors to emulate the sensory functionality of spider leg hairs, thereby enabling efficient acquisition and analysis of helmet wearing states. Unlike conventional vibration-based detection techniques, posture signals reflect spatial structural characteristics; however, their integration from multiple sensors introduces increased signal complexity and background noise. To address this issue, an improved adaptive convolutional neural network (ICNN) integrated with a long short-term memory (LSTM) network is employed to classify the tightness levels of the helmet chinstrap using both single-sensor and multi-sensor data. Experimental validation was conducted based on data collected from 20 participants performing wall-climbing robot operation tasks. The results demonstrate that the proposed method achieves a high recognition accuracy of 96%. This research offers a practical, privacy-preserving, and highly effective solution for helmet-wearing status monitoring in industrial environments. Full article

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. Full article

Electromagnetic articulography is a technique developed for recording three-dimensional movements. It is based on magnetic induction, where small currents are induced in miniature receiver coils acting as motion sensors by means of electromagnetic fields generated by transmitter coils. This technology has been applied in dental research to record mandibular movements during mastication, Posselt’s envelope of motion, and micromovements of dental prostheses. The AG501 electromagnetic articulograph (Carstens Medizinelektronik GmbH, Bovenden, Germany) provides a Head Correction (HC) procedure to eliminate head movement, which requires the reference sensors to be firmly attached to the subject’s head. If the sensors shift during the recordings, it becomes necessary to reposition them and repeat the head correction procedure. The aim of this study was to develop a 3D-printed helmet to securely fix the reference sensors to the head of a subject in the context of performing a series of recordings involving the mastication of 36 foods and the execution of Posselt’s envelope of motion. The number of HCs required was recorded for a group using the helmet and for a control group in which the sensors were attached to the subject’s head using tissue adhesive. A total of 29 recordings were conducted with and without the helmet. Without the helmet 44 HCs were required; on the other hand, with the helmet 36 HCs were required. On average, 1.5 HCs were required per session without the helmet and 1.2 HCs with the helmet, showing a non-significant difference (p < 0.05). A reduction in the number of HCs required per session was observed. However, more than one HC was still needed to complete a session. This could be addressed in future research by designing a series of helmets that adapt to different head sizes. Full article

The underground coal mine environment is complex and dynamic, making the application of visual algorithms for object detection a crucial component of underground safety management as well as a key factor in ensuring the safe operation of workers. We look at this in the context of helmet-wearing detection in underground mines, where over 25% of the targets are small objects. To address challenges such as the lack of effective samples for unworn helmets, significant background interference, and the difficulty of detecting small helmet targets, this paper proposes a novel underground helmet-wearing detection algorithm that combines dynamic background awareness with a limited number of valid samples to improve accuracy for underground workers. The algorithm begins by analyzing the distribution of visual surveillance data and spatial biases in underground environments. By using data augmentation techniques, it then effectively expands the number of training samples by introducing positive and negative samples for helmet-wearing detection from ordinary scenes. Thereafter, based on YOLOv10, the algorithm incorporates a background awareness module with region masks to reduce the adverse effects of complex underground backgrounds on helmet-wearing detection. Specifically, it adds a convolution and attention fusion module in the detection head to enhance the model’s perception of small helmet-wearing objects by enlarging the detection receptive field. By analyzing the aspect ratio distribution of helmet wearing data, the algorithm improves the aspect ratio constraints in the loss function, further enhancing detection accuracy. Consequently, it achieves precise detection of helmet-wearing in underground coal mines. Experimental results demonstrate that the proposed algorithm can detect small helmet-wearing objects in complex underground scenes, with a 14% reduction in background false detection rates, and thereby achieving accuracy, recall, and average precision rates of 94.4%, 89%, and 95.4%, respectively. Compared to other mainstream object detection algorithms, the proposed algorithm shows improvements in detection accuracy of 6.7%, 5.1%, and 11.8% over YOLOv9, YOLOv10, and RT-DETR, respectively. The algorithm proposed in this paper can be applied to real-time helmet-wearing detection in underground coal mine scenes, providing safety alerts for standardized worker operations and enhancing the level of underground security intelligence. Full article

This study addresses three major challenges in non-motorized vehicle rider helmet detection: multi-spectral interference between the helmet and hair color (HSV spatial similarity > 0.82), target occlusion in high-density traffic flows (with peak density reaching 11.7 vehicles/frame), and perception degradation under complex weather conditions (such as overcast, foggy, and strong light interference). To tackle these issues, we developed the GMAL-YOLO detection algorithm. This algorithm enhances feature representation by constructing a Feature-Enhanced Neck Network (FENN) that integrates both global and local features. It employs the Global Mamba Architecture Enhancement (GMET) to reduce parameter size while strengthening global context capturing ability. It also incorporates Multi-Scale Spatial Pyramid Pooling (MSPP) combined with multi-scale feature extraction to improve the model’s robustness. The enhanced channel attention mechanism with self-attention (ECAM) is designed to enhance local feature extraction and stabilize deep feature learning through partial convolution and residual learning, resulting in a 13.04% improvement in detection precision under occlusion scenarios. Furthermore, the model’s convergence speed and localization precision are optimized using the modified Enhanced Precision-IoU loss function(EP-IoU). Experimental results demonstrate that GMAL-YOLO outperforms existing algorithms on the self-constructed HelmetVision dataset and public datasets. Specifically, in extreme scenarios, the false detection rate is reduced by 17.3%, and detection precision in occluded scenes is improved by 13.6%, providing an effective technical solution for intelligent traffic surveillance. 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

The transportation sector is an important stakeholder in greenhouse gas emissions. Sustainable transportation systems come to the forefront against this problem, with the solutions within the scope of micro-mobility especially attracting attention for their environmentally friendly structures. While micro-mobility vehicles reduce the carbon footprint in transportation, their widespread use remains limited due to various security concerns. In this paper, an image processing-based process was carried out on vehicle and safety equipment usage to provide solutions to the security concerns of micro-mobility users. The effectiveness of frequently used data augmentation techniques was also examined to detect the presence of micro-mobility users and equipment usage with higher accuracy. In this direction, two different datasets (D1_Micro-mobility and D2_Helmet detection) and a total of 46 models were established and the effects of data augmentation techniques on YOLOv12 model performance outputs were evaluated with Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), one of the Multi-Criteria Decision-Making (MCDM) methods. In addition, the determination of Multiple Model Ensemble (MME), consisting of multiple data augmentation techniques, was also carried out through the K-means clustering–Elbow method. For D1_Micro-mobility datasets, it is observed that MME improves the model performance by 19.7% in F1-Score and 18.54% in mAP performance metric. For D2_Helmet detection datasets, it is observed that MME improves the model performance by 2.36% only in the Precision metric. The results show that, in general, data augmentation techniques increase model performance in a multidimensional manner. Full article

Background: Electric motorized rental scooters (ES) were introduced in Italy in 2019 as an alternative form of urban transportation, aiming to reduce traffic congestion and air pollution. As their popularity has grown, a parallel increase in ES-related injuries has been observed. This study aims to investigate the types and patterns of ES-related injuries and to identify potentially modifiable risk factors. Methods: We conducted a retrospective analysis of all consecutive patients admitted to the Emergency Department of Policlinico Umberto I in Rome between January 2020 and December 2022 following ES-related trauma. Collected data included demographics, injury mechanisms and types, helmet use, Injury Severity Score (ISS), blood alcohol levels, and patient outcomes. Results: A total of 411 individuals presented to the Emergency Department due to ES-related injuries, either as riders or pedestrians. The mean age was 31 years (range: 2–93); 38 patients (9%) were under 18 years of age. Fifty-six accidents (14%) occurred during work-related commutes. Only three riders (0.7%) wore helmets, and nine patients (2%) had blood alcohol levels > 0.50 g/L. Cranial injuries (134 cases, 32%) and upper limb fractures (93 cases, 23%) were the most frequently reported serious injuries. The mean ISS was 4.5; 17 patients (4%) had an ISS ≥ 16. A total of 270 orthopedic injuries and 118 (29%) maxillofacial injuries were documented. Head trauma was reported in 115 patients (28%), with 19 cases classified as severe traumatic brain injuries. Twenty-three patients (5.5%) were hospitalized, three (0.7%) required intensive care, and one patient (0.2%) died. Conclusions: ES-related injuries are becoming increasingly common and present a significant public health concern. A nationwide effort is warranted to improve rider safety through mandatory helmet use, protective equipment, alcohol consumption control, and stricter enforcement of speed regulations. Full article

Background and Objectives: Maxillofacial trauma represents a significant global health challenge with substantial physical, psychological, and socioeconomic consequences. Materials and Methods: This narrative review analyzed 112 articles published between 2000 and 2024 examining epidemiology, prevention, economics, and outcomes of maxillofacial trauma in critical care settings. Results: Road traffic accidents remain the primary cause globally, followed by interpersonal violence and occupational injuries. Effective prevention strategies include seat belt laws, helmet legislation, and violence prevention programs. Economic burden encompasses direct healthcare costs (averaging USD 55,385 per hospitalization), productivity losses (11.8 workdays lost per incident), and rehabilitation expenses (USD 3800–18,000 per patient). Surgical management has evolved toward early intervention, minimally invasive approaches, and advanced techniques using computer-aided design and 3D printing. Complications affect 3–33% of patients, with significant functional disabilities and psychological sequelae (post-traumatic stress disorder in 27%, depression/anxiety in 20–40%). Conclusion: Maxillofacial trauma management requires multidisciplinary approaches addressing both immediate treatment and long-term rehabilitation. Despite technological advances, disparities in specialized care access persist globally. Future efforts should implement evidence-based prevention strategies, reduce care disparities, and develop comprehensive approaches addressing physical, psychological, and socioeconomic dimensions through collaboration among healthcare professionals, policymakers, and community stakeholders. Full article

This study explores the influence of chinstrap stiffness in baseball helmets on brain stress distribution during high-velocity impacts through a computational biomechanical model integrating neuroanatomical structures and helmet components. Using a framework that combines finite element analysis and smoothed-particle hydrodynamics, this research evaluates fluid–structure interactions between cerebrospinal fluid, brain tissue, and six chinstrap configurations ranging from highly flexible to non-stretchable. The results reveal a critical trade-off: highly flexible straps reduce intracranial stress by dissipating energy through viscoelastic deformation but compromise helmet stability, while non-stretchable designs transmit undampened forces directly to the skull base, amplifying stress in vulnerable neurovascular regions. Intermediate stiffness configurations introduce a hazardous instability regime, where partial decoupling between the helmet and mandible causes lateral sliding of the chin guard, concentrating stresses at bony interfaces. The study identifies a nonlinear relationship between material rigidity and neuroprotection, emphasizing that optimal chinstrap design must balance elasticity to absorb impact energy with sufficient rigidity to maintain alignment and prevent stress redirection. Intermediate stiffness thresholds, despite partial energy absorption, paradoxically heighten risks due to incomplete coupling and dynamic instabilities. These findings challenge conventional helmet design paradigms, advocating for material engineering strategies that prioritize energy dissipation pathways while avoiding detrimental intermediate stiffness ranges. The insights advance concussion mitigation by refining chinstrap performance criteria to address both direct force transmission and instability-mediated injury mechanisms. Full article

Helmets are crucial for protecting motorcycle riders from head injuries in accidents. This study proposes a helmet pad design based on a negative-Poisson’s-ratio (NPR) structure and comprehensively evaluates its protective effect on head injuries. A concave hexagonal honeycomb structure was embedded into the energy-absorbing lining of a motorcycle helmet, and finite element collision simulations were conducted according to the ECE R22.05 standard. These simulations compared and analyzed the differences in protective performance between concave hexagonal honeycomb helmets with different parameter configurations and traditional expanded polystyrene (EPS) helmets under flat anvil impact scenarios. Using biomechanical parameters, including peak linear acceleration (PLA), head injury criterion (HIC), intracranial pressure (ICP), maximum principal strain (MPS), and the probability of AIS2+ traumatic brain injury, the protective effect of the helmets on traumatic brain injury was evaluated. The results showed that when the wall angle of the honeycomb structure was 60°, honeycomb helmets with wall thicknesses of 0.8 mm and 1.0 mm significantly reduced PLA and HIC values. In particular, the honeycomb helmet with a wall thickness of 1.0 mm reduced ICP by 25.7%, while the honeycomb helmet with a wall thickness of 1.2 mm exhibited the lowest maximum principal strain in the skull compared to EPS helmets and reduced the probability of AIS2+ brain injury by 7.2%. Concave hexagonal honeycomb helmets demonstrated an excellent protective performance in reducing the risk of traumatic brain injury. These findings provide important theoretical foundations and engineering references for the design and optimization of new protective helmets. Full article

$\mathrm{Visible}\mathrm{light}\mathrm{communication}$ (VLC) is an emerging technology that offers an alternative to traditional wireless communications systems. However, the technology presents limitations related to the impact of the receiver’s orientation, which can significantly impact its performance. To address this issue, VLC systems use diversity schemes, such as transmitter and receiver diversity. In this paper, we derive an analytical expression for the probability of maintaining a line-of-sight (LoS) link in an underground mining visible light communication (UM-VLC) system with a receiver embedded in an object, such as a helmet, by considering user mobility. We show that the angle of incidence depends on the distance from the source and derive the probability accordingly for single-input single-output (SISO), multiple-input single-output (MISO), and single-input multiple-output cases (SIMO). Our results show that the analytical results fit with the simulated results. Furthermore, the resulting probabilities show that the angular position of the receiver significantly affects the channel’s quality, with the optimal position dependent on the field-of-view characteristics. These findings can provide an appropriate framework for receiver and transmitter diversity design through analytical expression. 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