BackgroundCertification standards governing short track (ST) helmets only require high velocity impacts be tested. Rotational acceleration and low velocity impacts are mechanisms of injury which are ...known to cause concussion. Conversely, ice hockey (IH) helmet certification require low velocity impacts in addition to high velocity impacts, and have been designed to mitigate both impact velocities.ObjectiveTo compare the impact attenuation characteristics between ST and IH helmets, in both high and low velocity impacts.DesignTwo-group experimental design.SettingImpacts were performed in laboratory under controlled conditions. Helmets were impacted at two impact velocities (high and low; 4.5m/s and 2.4m/s respectively) and four impact locations (rear, rear boss, side and front boss). This was performed using a linear impactor device and the Hybrid III surrogate headform and neck.Patients (or Participants)5 different helmet models; 3 ST models and 2 IH models.Interventions (or Assessment of Risk Factors)Assessment of ST and IH helmet impact attenuation under various conditions.Main Outcome MeasurementsPeak linear and rotational acceleration; Head Injury Criterion (HIC) and Brain Injury Criterion (BrIC).ResultsBetween-groups ANOVA for linear Low F(1,27) = 10.7, p<0.05, η2 = 0.284; High = F(1,24) = 5.8, p<0.05, η2 = 0.195 and rotational Low F(1,27) = 15.8, p<0.05, η2 = 0.370; High = F(1,24) = 8.1, p<0.05, η2 = 0.251 accelerations yielded statistically significant differences with large effect sizes for all impact locations in both impact velocities. One-way between-helmet ANOVAs and post-hoc Bonferroni revealed impact attenuation performance hierarchy: IH 2 > IH 1 > ST 3 > ST 1 > ST 2. Between-groups ANOVA revealed statistical differences for HIC Low F(1,27) = 14.1, p<0.05, η2 = 0.344; High = F(1,24) = 7.6, p<0.05, η2 = 0.241. BrIC results were mixed.ConclusionsResults suggest that these IH helmets are better at attenuating both impact velocities than this group of ST helmets. Interestingly, the largest effect sizes were observed in the low-velocity impacts.
ObjectiveTo compare the impact attenuation characteristics between ST and IH helmets, in both high and low velocity impacts.DesignTwo-group experimental design.SettingImpacts were performed in ...laboratory under controlled conditions. Helmets were impacted at two impact velocities (high and low; 4.5m/s and 2.4m/s respectively) and four impact locations (rear, rear boss, side and front boss). This was performed using a linear impactor device and the Hybrid III surrogate headform and nec.Participants5 different helmet models; 3 ST models and 2 IH models.Interventions (or Assessment of Risk Factors)Assessment of ST and IH helmet impact attenuation under various conditions.Outcome MeasuresPeak linear and rotational acceleration; Head Injury Criterion (HIC) and Brain Injury Criterion (BrIC).Main ResultsBetween-groups ANOVA for linear Low F(1,27) = 10.7, p<0.05, η2 = 0.284; High = F(1,24) = 5.8, p<0.05, η2 = 0.195 and rotational Low F(1,27) = 15.8, p<0.05, η2 = 0.370; High = F(1,24) = 8.1, p<0.05, η2 = 0.251 accelerations yielded statistically significant differences with large effect sizes for all impact locations in both impact velocities. One-way between-helmet ANOVAs and post-hoc Bonferroni revealed impact attenuation performance hierarchy: IH 2 > IH 1 > ST 3 > ST 1 > ST 2.Between-groups ANOVA revealed statistical differences for HIC Low F(1,27) = 14.1, p<0.05, η2 = 0.344; High = F(1,24) = 7.6, p<0.05, η2 = 0.241. BrIC results were mixed.ConclusionsResults suggest that these IH helmets are better at attenuating both impact velocities than this group of ST helmets. Interestingly, the largest effect sizes were observed in the low-velocity impacts.
Introduction:
We seek to characterize unhelmeted injured cyclists presenting to the emergency department (ED): demographics, cycling behaviour, and attitudes towards helmet use.
Methods:
This was a ...prospective cohort study in a downtown teaching hospital, from May 2016 - Sept 2019. Injured cyclists presenting to the ED were recruited if they were not wearing a helmet at time of injury and over age 18. Exclusion criteria included intoxication, inability to consent, or admission to hospital. A standardized survey was administered by a research coordinator. Descriptive statistics were used to summarize the data, and survey responses reported as percentages.
Results:
We surveyed a convenience sample of 68 unhelmeted injured cyclists (UICs) with mean age of 33.6 years (range 18 to 68, median 29.5 years). Ratio of males to females was 1:1. The majority of UICs cycled most days per week or every day in non-winter months (89.6 %, n = 60). Cycling in Toronto was perceived as somewhat dangerous (45.6%, n = 31) or very dangerous (5.9%, n = 4) by most, and very safe (2.94 %, n = 2) or somewhat safe (19.12%, n = 13) by few. Almost a third (29.4 %, n = 20) had been in a cycling accident in the prior year, some of these (15.0%, n = 3) prompting an ED visit. All cyclists were riding their personal bike (100 %, n = 68) at time of injury, and most (98.5%, n = 67) had planned to cycle when they departed home that day. Purpose of trip was primarily for commuting to work (50%, n = 34), social activities (19.1%, n = 13), school (7.4%, n = 5), and recreation (7.4%, n = 5). Bicycle helmet ownership was low (41.2 %, n = 28). UICs reported rarely (10.3%, n = 7) or never (64.7%, n = 44) wearing a helmet when cycling. Reported factors discouraging helmet use included inconvenience (33.8%, n = 23), lack of ownership (32.4%, n = 22), discomfort (29.4%, n = 20), and ‘messed hair’ (14.7%, n = 10). Few characterized helmets as unnecessary (10.3%, n = 7) or ineffective (1.5%, n = 1). The majority had a college diploma or more advanced education (77.9%, n = 53), and spoke English at home (85.3%, n = 58).
Conclusion:
Unhelmeted injured cyclists surveyed were frequent commuter cyclists who do not regard cycling as safe, yet choose not to wear helmets for reasons largely related to convenience rather than perceptions regarding safety or necessity. Initiatives to increase helmet use in this subgroup should address the reasons given for not wearing a helmet, potentially using principles of adult education and behavioral economics.
According to the WHO, Thailand has the second most dangerous roads in the world over 2600 children die and more than 72 000 are injured on Thailand’s roads every year. However, only 7% of children ...wear helmets when riding motorcycles, even when their parents do. The 7% Project, in partnership of Save the Children Thailand and the Asia Injury Prevention (AIP) Foundation that focuses on behavior influence strategies, education, and enforcement to increase helmet use and helmet-wearing awareness among children, ultimately decreasing the number of road crash injuries and fatalities across Thailand. In collaboration with Bangkok Metropolitan Administration (BMA), The 7% Project aims to implement its education component on Behavior Influence (BI) activities for student helmet use promotion in 6 pilot schools while monitoring the helmet use situations in other 6 control schools. Therefore, Save the Children International has supported the ThaiRoads Foundation to conduct student helmet use observation at those 12 schools for use as a baseline information as well as monitoring the project progress. In this study, the Quasi-Experimental Designs was use to evaluate the effect of the educational campaign for student helmet use promotion under The 7% Project, using the survey data at pilot and control schools from May to November 2015. The result suggests that during the monitoring periods, the educational campaign could lead to a relative increase in student helmet use rates that could be as high as 133% in September 2015. However, the use of data from the fourth or final observation in November 2015 reveals a significant decline in the estimated effect of behavior influence activities to 45%, suggesting that further student helmet use observations would be necessary to examine the sustainability of the effect.
Abstract
Aiming at problems of low accuracy and strong detection interference of the existing safety helmet wearing detection algorithms, an object detection algorithm by adding the ...squeeze-and-excitation block based on the YOLOv5 algorithm is proposed in this paper. The proposed method can not only obtain the weight of picture channel, but also accurately separate the foreground and background of the picture. Keeping all parameters unchanged, the proposed method and the YOLOv5 algorithm are applied to detect the safety helmet data set in the experiment. The result shows that the YOLOv5 algorithm with the squeeze-and-excitation block has an average detection accuracy of 94.5% for safety helmets and an average detection accuracy of 92.7% for human heads. The mAP value detected by the proposed method is 2% ∼2.5% higher than using YOLOv5 algorithm directly.
The number of motorcyclists in Wales has reached record highs and, while accounting for only 0.7% of the vehicles in Wales, they accounted for ~35% of the injuries categorised as killed or seriously ...injured. Most studies in the literature have shown that the use of motorcycle helmets reduces the probability of brain injury and death, with strong support for their use from international bodies such as the world health organisation. This work aimed to improve motorcyclist head protection by augmenting the single impact performance of existing helmets with multi-impact mitigation. The following objectives supported this aim: An approach to improve elastomeric Fused Filament Fabrication (FFF) manufacturing quality was developed, and an equivalent porosity to injection moulding components was demonstrated. A novel accessible approach, using a uniaxial test machine to characterise elastomers dynamically, was developed. A novel computational method to generate elastomeric rate-dependent energy absorption diagrams was also developed. Additionally, the ability to scale these diagrams between different base elastomers was demonstrated. After selecting a preliminary configuration from an energy absorption diagram, a subsequent simplified simulation of a motorcycle helmet impact enabled efficient optimisation. This approach was successfully used to predict the response of a more complex helmet assembly. A similar agreement between simulation and experimental work was observed for this approach, as was observed when simulating a fully modelled helmet assembly. A prototype helmet, containing an elastomeric cellular structure, was shown to repeatedly pass the requirements of UNECE 22.05 while demonstrating a consistent co-efficient of restitution equivalent to that of an expanded polystyrene (EPS) helmet, even as shell failure occurred. The prototype helmet met the requirements of UNECE 22.05 at three of the four investigated locations. Additionally, it exceeded EPS' performance at one location with a liner thickness of 70% that of EPS.
AbstractThe construction site environment is quite complex with many dangerous hazards (e.g., foundation pits, holes). To avoid injuries, workers must wear helmets that are color-coded for the ...specific type of work, which is helpful to identify whether workers are in permitted areas. Therefore, it is possible to identify unauthorized intrusion by classifying the safety helmets. This study proposes a vision-based method called Helmet–Yolov5 to automatically detect unauthorized intrusions by workers on construction sites. Multiple improvement measures are made to enhance the model performance. First, the attention mechanism is used to enhance the weights of object regions in the image, which makes the detection of small objects more effective. Second, atrous spatial pyramid pooling is adopted to preserve the detail information of the image. Third, the universal upsampling operator is introduced to fuse image features at different scales. To verify the effectiveness of the improved model, images collected from a real construction site are used to build a large-scale image dataset of safety helmets for model testing. It shows that the proposed Helmet-Yolov5 model is more accurate than the original Yolov5 model, also with high inference speed. Compared to other state-of-the-art models (e.g., Yolov4), the Helmet-Yolov5 model has considerable advantages in term of high detection accuracy and efficiency.
ObjectiveCurrent equestrian helmet standards do not include angular acceleration for certification even though it is known that it is the dominant cause of brain injury. Therefore, the objective was ...to develop an improved test method, including oblique impacts, to evaluate helmets sold on the European market.DesignThe study presents a novel method to preform consumer testing of equestrian helmets.SettingFour physical tests were conducted, shock absorption with straight perpendicular impact and three oblique impact tests. Computer simulations based on the test measurements were made to evaluate injury risk.ParticipantsIn total, 28 equestrian helmet models sold on the European market were included.Interventions (or Assessment of Risk Factors)Identify the best preforming helmet on the European market.Outcome MeasuresLinear acceleration (g) and the strain in the grey matter of a finite element brain model were used to estimate the risk of brain injuries. Helmets equipped with and without Multi-directional Impact Protection System (MIPS).Main ResultsIn only two helmets a linear acceleration lower than 180 g was measured, which corresponds to a low risk of skull fracture. The simulations indicated that the strain in the grey matter of the brain during oblique impacts varied between helmets from 16% to 51%, where 26% corresponds to 50% risk for a concussion. The two helmets equipped with MIPS performed in general better than the others.ConclusionsAll helmets need to reduce rotational acceleration more effectively. A helmet that meets the current standards does not necessarily prevent concussion.
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