Search Results (16)

Background: Pediatric road traffic injuries (RTIs) represent a significant public health concern, particularly in countries like Romania, where road infrastructure and safety remain challenges. Despite recent economic reclassification, Romania continues to report high rates of pediatric traffic-related injuries. Non-fatal RTIs often result in long-term physical and psychological harm. This study aims to assess age- and gender-specific injury patterns and mechanisms of non-fatal RTIs in children and adolescents, using data from “St. Mary’s” Emergency Clinical Hospital for Children in Iași over a ten-year period to inform targeted prevention strategies. Methods: This 10-year retrospective study (2015–2024) was conducted at “St. Mary’s” Emergency Clinical Hospital for Children in Iași, Romania, a regional referral center. Data from 1074 pediatric patients (aged 1 month–17 years, 11 months) with RTIs were analyzed using ICD-10 codes and verified manually. Variables included demographics, injury type, mechanism, and treatment. Patients were stratified into four age groups. Statistical analysis was performed using IBM SPSS Statistics 25, with significance set at p < 0.05. Results: The highest incidence was observed among boys (77.7%) and children aged 10–14 years. Car passengers and cyclists constituted the most frequently affected groups, with only 11% of passengers appropriately restrained and 78% of cyclists not wearing helmets. Common injuries included excoriations, thoracic contusions, and abdominal trauma, with notable variations by age and sex. Thoracic injuries were more frequent among girls, whereas younger children exhibited a higher incidence of abdominal trauma. Conclusions: The findings emphasize critical safety gaps in child restraint and helmet use and highlight the urgent need for targeted, age-specific road safety interventions and improved public health education. Full article

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

Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively. Full article

Visual inspection of the workplace and timely reminders of unsafe behaviors (e.g, not wearing a helmet) are particularly significant for avoiding injuries to workers on the construction site. Video surveillance systems generate large amounts of non-structure image data on site for this purpose; however, they require real-time recognition automation solutions based on computer vision. Although various deep-learning-based models have recently provided new ideas for identifying helmets in traffic monitoring, few solutions suitable for industry applications have been discussed due to the complex scenarios of construction sites. In this paper, a fast and robust network based on a mutilscale Swin Transformer is proposed for safety helmet detection (FRSHNet) at construction sites, which contains the following contributions. Firstly, MAE-NAS with the variant of MobileNetV3’s MobBlock as a basic block is applied to implement feature extraction. Simultaneously, a multiscale Swin Transformer module is utilized to obtain the spatial and contexture relationships in the multiscale features. Subsequently, in order to meet the scheme requirements of real-time helmet detection, efficient RepGFPN are adopted to integrate refined multiscale features to form a pyramid structure. Extensive experiments were conducted on the publicly available Pictor-v3 and SHWD datasets. The experimental results show that FRSHNet consistently provided a favorable performance, outperforming the existing state-of-the-art models. Full article

Sport-related concussion prevention strategies in collision sports are a primary interest for sporting organizations and policy makers. After-market soft-shell padding purports to augment the protective capabilities of standard football helmets and to reduce head impact severity. We compared head impact kinematics [peak linear acceleration (PLA) and peak rotational acceleration (PRA)] in athletes wearing Guardian Cap soft-shell padding to teammates without soft-shell padding. Ten Division I college football players were enrolled [soft-shell padding (SHELL) included four defensive linemen and one tight end; non-soft-shell (CONTROL) included two offensive linemen, two defensive linemen, and one tight end]. Participants wore helmets equipped with the Head Impact Telemetry System to quantify PLA (g) and PRA (rad/s²) during 14 practices. Two-way ANOVAs were conducted to compare log-transformed PLA and PRA between groups across helmet location and gameplay characteristics. In total, 968 video-confirmed head impacts between SHELL (n = 421) and CONTROL (n = 547) were analyzed. We observed a Group x Stance interaction for PRA (F_1,963 = 7.21; p = 0.007) indicating greater PRA by SHELL during 2-point stance and lower PRA during 3- or 4-point stances compared to CONTROL. There were no between-group main effects. Protective soft-shell padding did not reduce head impact kinematic outcomes among college football athletes. Full article

Personal protective equipment (PPE) can increase the safety of the worker for sure by reducing the probability and severity of injury or fatal incidents at construction, chemical, and hazardous sites. PPE is widely required to offer a satisfiable safety level not only for protection against the accidents at the aforementioned sites but also for chemical hazards. However, for several reasons or negligence, workers may not commit to and comply with the regulations of wearing the equipment, occasionally. Since manual monitoring is laborious and erroneous, the situation demands the development of intelligent monitoring systems to offer the automated real-time and accurate detection of PPE compliance. As a solution, in this study, Deep Learning and Computer Vision are investigated to offer near real-time and accurate PPE detection. The four colored hardhats, vest, safety glass (CHVG) dataset was utilized to train and evaluate the performance of the proposed model. It is noteworthy that the solution can detect eight variate classes of the PPE, namely red, blue, white, yellow helmets, head, person, vest, and glass. A two-stage detector based on the Fast-Region-based Convolutional Neural Network (RCNN) was trained on 1699 annotated images. The proposed model accomplished an acceptable mean average precision (mAP) of 96% in contrast to the state-of-the-art studies in literature. The proposed study is a potential contribution towards the avoidance and prevention of fatal/non-fatal industrial incidents by means of PPE detection in real-time. Full article

International university students are vulnerable travellers due to their unpredictable schedules and lifestyles. As Thailand continues to see an increase in international students, evaluating their pre-travel preparation and preventive behaviours is crucial to identify areas for improvement. For this purpose, an online survey focusing on pre-travel preparation, knowledge and preventive practices related to travel health was distributed to 324 eligible international students from 14 Thai universities, with the majority being from Asia and Oceania (79.0%; n = 256). The results showed that half of the respondents (53.7%; n = 175) received professional pre-travel advice, mainly because of the mandatory health examination and vaccination requirements of the host university. The study also revealed inadequate knowledge about infectious and non-infectious health risks, with only one-third being aware that Japanese encephalitis is transmitted by mosquito bites, and less than half of the students recognising Thailand’s emergency services number. Poor preventive practices were also observed, with less than half of those with new sexual partners consistently using condoms and less than half of those riding motorcycles always wearing helmets. These findings highlight the need for a new strategy to improve the standard of travel health preparation among this group of young adult travellers, particularly those from resource-limited countries. Full article

Alpine skiing is characterized by specific and dynamic conditions and demands constant processing of visual information and fast decision-making. A fast response time is necessary for protective movements which reduce the number and severity of additional head impacts. The apparent detriments to visual performance caused by protective headgear are concerning and should be considered moving forward in recreational alpine skiing. The aim of this study was to examine the effects of wearing the three most common combinations of protective headgear in skiing on the timing of visual stimuli perception and adequate response when simulating on-the-slope situations. The sample consisted of 45 recreational-level skiers (27 M, 18 F; age 30.6 ± 8.19 years) who had finished basic alpine skiing school, had been skiing 6–10 years continuously, and were students of Faculty of Kinesiology, University of Zagreb. They did not report any serious medical conditions regarding vision. The overall testing was conducted in the winter season during January and February of 2022. Reaction time on perceived visual stimuli was observed in a way that a skier was approaching behind a participant’s back from both the left and right side. A 2 × 3 (helmet*condition) mixed-model repeated-measures ANOVA was used to determine differences between helmet users and non-users in each tested condition. When observing the results, it was confirmed that the response time of the participants was the slowest when wearing a ski helmet and goggles combined. Furthermore, one of the most important findings was the determined differences in reaction time between helmet users and non-users, i.e., prior helmet users tended to react faster to the upcoming visual stimuli when wearing combined ski helmet and goggles. In the design and construction of the goggles, it is also necessary to pay attention to reducing the thickness of the frame in order to reduce the distance between the eye and the lens, which consequently reduces interference in the peripheral parts of the field of vision. In future studies, the same testing protocol with all the possible combinations of wearing a ski cap, a helmet, sunglasses, and goggles is necessary to gain a clearer insight into the effect of each item of headgear separately and in various combinations. Full article

Crashes involving vulnerable road users (VRUs) are types of traffic accidents which take up a large proportion and cause lots of casualties. With methods of statistics and accident reconstruction, this research investigates 378 actual traffic collisions between vehicles and VRUs in China in 2021 to obtain human, vehicle, and road factors that affect the injury severity. The paper focuses on risky behaviors of VRUs and typical scenarios such as non-use of the crosswalk, violation of traffic lights, stepping into the motorway, and riding against traffic. Then, based on the Bayesian General Ordinal Logit model, influencing factors of injury severity in 168 VRU accidents are analyzed. Results demonstrate that the probability of death in an accident will rise when the motorist is middle-aged and the VRU is an e-bicycle rider; the probability of death in an accident will greatly decrease when the VRU bears minor responsibility. Therefore, middle-aged motorists and e-bicycle riders should strengthen safety consciousness and compliance with regulations to prevent accident and reduce injury for VRUs. In addition, helmet-wearing will help to reduce riders’ injuries. This research may provide ideas for intelligent vehicles to avoid collisions with risky VRUs. Full article

The benefit of protective headgear for recreational skiers is an ongoing debate in the snow sports industry, and there are a lot of opposing opinions. Due to the dynamic conditions in which winter sports are performed, athletes demand rapid and constant processing of visual information. A sufficient level of anticipation helps athletes to properly position themselves to reduce the forces transferred to the head or even move to avoid a collision. To objectively identify the impact of protective headgear on the visual field when skiing, it is necessary to conduct suitable measurements. The sample consisted of 43 recreational-level skiers (27 M, 16 F; age 31.6 ± 8.23 years). A predefined testing protocol on an ortoreter was used to assess the visual field for three conditions of wearing protective headgear. Differences in perceived visual stimuli between the three conditions were evaluated by repeated measures analysis of variance (ANOVA). Based on the observed results, it can be concluded that the combination of wearing a ski helmet and ski goggles significantly negatively influences visual performance in a way that the visual field is narrowed, for both helmet users and non-users, only when comparing the tested conditions. When comparing helmet users and non-users, there are no differences in the amount of visual impairment; therefore, the habit of wearing a helmet does not influence the ability of perceiving visual stimuli. Full article

Not wearing a helmet, not properly strapping the helmet on, or wearing a substandard helmet increases the risk of fatalities and injuries in motorcycle crashes. This research examines the differences in motorcycle crash injury severity considering crashes involving the compliance with and defiance of helmet use by motorcycle riders and highlights the temporal variation in their impact. Three-year (2017–2019) motorcycle crash data were collected from RESCUE 1122, a provincial emergency response service for Rawalpindi, Pakistan. The available crash data include crash-specific information, vehicle, driver, spatial and temporal characteristics, roadway features, and traffic volume, which influence the motorcyclist’s injury severity. A random parameters logit model with heterogeneity in means and variances was evaluated to predict critical contributory factors in helmet-wearing and non-helmet-wearing motorcyclist crashes. Model estimates suggest significant variations in the impact of explanatory variables on motorcyclists’ injury severity in the case of compliance with and defiance of helmet use. For helmet-wearing motorcyclists, key factors significantly associated with increasingly severe injury and fatal injuries include young riders (below 20 years of age), female pillion riders, collisions with another motorcycle, large trucks, passenger car, drivers aged 50 years and above, and drivers being distracted while driving. In contrast, for non-helmet-wearing motorcyclists, the significant factors responsible for severe injuries and fatalities were distracted driving, the collision of two motorcycles, crashes at U-turns, weekday crashes, and drivers above 50 years of age. The impact of parameters that predict motorcyclist injury severity was found to vary dramatically over time, exhibiting statistically significant temporal instability. The results of this study can serve as potential motorcycle safety guidelines for all relevant stakeholders to improve the state of motorcycle safety in the country. Full article

Background: Summaries of systematic reviews are a reference method for the dissemination of research evidence on the effectiveness of public health interventions beyond the scientific community. Motivated reasoning and cognitive dissonance may interfere with readers’ ability to process the information included in such summaries. Methods: We conducted a web experiment on a panel of university-educated North Americans (N = 259) using a systematic review of the effectiveness of bicycle helmet legislation as a test case. The outcome variables were the perceived tentativeness of review findings and attitude toward bicycle helmet legislation. We manipulated two types of uncertainty: (i) deficient uncertainty (inclusion vs. non-inclusion of information on limitations of the studies included in the review) and (ii) consensus uncertainty (consensual findings showing legislation effectiveness vs. no evidence of effectiveness). We also examined whether reported expertise in helmet legislation and the frequency of wearing a helmet while cycling interact with the experimental factors. Results: None of the experimental manipulations had a main effect on the perceived tentativeness. The presentation of consensual efficacy findings had a positive main effect on the attitude toward the legislation. Self-reported expertise had a significant main effect on the perceived tentativeness, and exposing participants with reported expertise to results showing a lack of evidence of efficacy increased their favorable attitude toward the legislation. Participants’ helmet use was positively associated with their attitude toward the legislation (but not with perceived tentativeness). Helmet use did not interact with the experimental manipulations. Conclusions: Motivated reasoning and cognitive dissonance influence a reader’s ability to process information contained in a systematic review summary. Full article

The Electric Bike (EB) has become an ideal mode of transportation because of its simple operation, convenience, and because it is time saving, economical and environmentally friendly. However, electric bicycle road-traffic injuries (ERTIs) have become a road-traffic safety problem that needs to be solved urgently, bringing a huge burden to public health. In order to provide basic data and a theoretical basis for the prevention and control of ERTIs in Shantou, mixed research combining a case-control study and a case-crossover study was carried out to investigate the cycling behavior characteristics and injury status of EB riders in Shantou city, and to explore the influencing factors of ERTI. The case-control study selected the orthopedic inpatient departments of three general hospitals in Shantou. The case-crossover study was designed to assess the effect of brief exposure on the occurrence of ERTIs, in which each orthopedic inpatient serves as his or her own control. Univariable and multivariable logistic regressions were used to examine the associated factors of ERTIs. In the case-control study, multivariable analysis showed that chasing or playing when cycling, finding the vehicle breakdown but continuing cycling, not wearing the helmet, and retrograde cycling were risk factors of ERTIs. Compared with urban road sections, suburb and township road sections were more likely to result in ERTIs. Astigmatism was the protective factor of ERTI. The case-crossover study showed that answering the phone or making a call and not wearing a helmet while cycling increased the risk of ERTIs. Cycling in the motor-vehicle lane and cycling on the sidewalk were both protective factors. Therefore, the traffic management department should effectively implement the policy about wearing a helmet while cycling, increasing the helmet-wearing rate of EB cyclists, and resolutely eliminate illegal behaviors such as violating traffic lights and using mobile phones while cycling. Mixed lanes were high-incidence road sections of ERTIs. It was suggested that adding people-non-motor-vehicles/motor vehicles diversion and isolation facilities in the future to ensure smooth roads and safety would maximize the social economic and public health benefits of EB. Full article

Aimed at preventing heat strain, health problems, and absenteeism among workers with physically demanding occupations, a continuous, accurate, non-invasive measuring system may help such workers monitor their body (core) temperature. The aim of this study is to evaluate the accuracy and explore the usability of the wearable non-invasive Cosinuss° °Temp thermometer. Ear canal temperature was monitored in 49 workers in real-life working conditions. After individual correction, the results of the laboratory and field study revealed high correlations compared to ear canal infrared thermometry for hospital use. After performance of the real-life working tasks, this correlation was found to be moderate. It was also observed that the ambient environmental outdoor conditions and personal protective clothing influenced the accuracy and resulted in unrealistic ear canal temperature outliers. It was found that the Cosinuss° °Temp thermometer did not result in significant interference during work. Therefore, it was concluded that, without a correction factor, the Cosinuss° °Temp thermometer is inaccurate. Nevertheless, with a correction factor, the reliability of this wearable ear canal thermometer was confirmed at rest, but not in outdoor working conditions or while wearing a helmet or hearing protection equipment. Full article

Appropriate use of helmets as industrial personal protective gear is a long-standing challenge. The dilemma for any user wearing a helmet is thermal discomfort versus the chances of head injuries while not wearing it. Applying helmet microclimate psychrometry, we propose a logistic regression- (LR) based machine learning (ML) algorithm coupled with low-cost and readily available MEMS sensors to determine if a helmet was worn (W) or not worn (NW) by a human user. Experiment runs involving human subject (S) and mannequin experiment control (C) groups were conducted across no mask (NM) and mask (M) conditions. Only ambient-microclimate humidity difference (AMHD) was a feasible parameter for helmet wearing determination with 71 to 85% goodness of fit, 72 to 76% efficacy, and distinction from control group. Ambient-microclimate humidity difference’s rate of change (AMHDROC) had high correlation to helmet wearing and removal initiations and was quantitatively better in all measures. However, its feasibility was doubtful for continuous use beyond 1 min due to plateauing AMHD response. Experiments with control groups and temperature measurement showed invariant response to helmet worn or not worn with goodness of fit and efficacy consolidation to 50%. Results showed the algorithm can make helmet-wearing determinations with combination of analysis and use of data that was individually authentic and non-identifiable. This is an improvement as compared to state of the art skin-contact mechanisms and image analytics methods in enabling safety enhancements through data-driven worker safety ownership. Full article