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Background Morbidity and mortality from road traffic crashes are steadily increasing globally and they remain a major public health challenge. This burden is disproportionately borne by low-and middle-income countries, especially Sub-Saharan Africa where motorcycle helmet use is low and where there are challenges of affordability and availability of standard helmets. We sought to assess the availability and cost of helmets in retail outlets in northern Ghana. Methods A market survey of 408 randomly sampled automobile-related retail outlets in Tamale, northern Ghana was conducted. Multivariable logistic regression was used to identify factors associated with helmet availability and gamma regression was used to identify factors associated with their cost. Results Helmets were available in 233 (57.1%) of surveyed retail outlets. On multivariable logistic regression, street vendors were 48% less likely and motorcycle repair shops 86% less likely to sell helmets than automobile/motorcycle shops. Outlets outside the Central Business District were 46% less likely to sell helmets than outlets inside that district. Nigerian retailers were five times more likely to sell helmets than Ghanaian retailers. Median helmet cost was 8.50 USD. Helmet cost decreased by 16% at street vendors, 21% at motorcycle repair shops, and 25% at outlets run by the owner. The cost increased by older age of retailer (1% per year of age), education level of retailer (12% higher for secondary education, 56% higher for tertiary education, compared to basic education), and sex (14% higher for male retailer). Conclusion Motorcycle helmets were available in some retail outlets in northern Ghana. Efforts to improve helmet availability should address outlets in which they are less commonly sold, including street vendors, motorcycle repair shops, outlets run by Ghanaians, and outlets outside the Central Business District.

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.

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.

Correspondence to Dr Paul McCrory, The Florey Institute of Neuroscience and Mental Health, Heidelberg 3084, Victoria, Australia; paulmccrory@icloud.com Preamble The 2017 Concussion in Sport Group (CISG) consensus statement is designed to build on the principles outlined in the previous statements1–4 and to develop further conceptual understanding of sport-related concussion (SRC) using an expert consensus-based approach. First and foremost, this document is intended to guide clinical practice; however, the authors feel that it can also help form the agenda for future research relevant to SRC by identifying knowledge gaps. At present, there is no perfect diagnostic test or marker that clinicians can rely on for an immediate diagnosis of SRC in the sporting environment. Because of this evolving process, it is not possible to rule out SRC when an injury event occurs associated with a transient neurological symptom. [...]tests include the SCAT5, which incorporates the Maddocks' questions6 7 and the Standardised Assessment of Concussion (SAC).8–10 It is worth noting that standard orientation questions (eg, time, place, person) are unreliable in the sporting situation when compared with memory assessment.7 11 It is recognised, however, that abbreviated testing paradigms are designed for rapid SRC screening on the sidelines and are not meant to replace a comprehensive neurological evaluation; nor should they be used as a standalone tool for the ongoing management of SRC.

This paper proposes a research method to enhance the accuracy and real-time capability of helmet detection in complex industrial environments, aiming to address the engineering challenges of poor robustness and significant occurrences of both false positives and false negatives in existing detection methods. In this study, the C2F (faster version of CSP Bottleneck with two convolutions) module and FE (FasterNet with EMA) module are integrated into the network architecture of YOLOV8 to form a new attention mechanism module called C2F-FE. This module enhances the model’s perception of safety helmet targets by fusing feature information from different levels and incorporating attention mechanisms while reducing computational overhead. Furthermore, the model is trained and optimized on publicly available safety helmet datasets. Experimental results demonstrate that the improved model exhibits stronger robustness, achieving an accuracy rate of 94.6% and a mAP50 of 99.1% for safety helmet detection in complex construction scenarios, with an inference time of 0.7 ms.

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.