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Many factors are considered during ballistic helmet design, including comfort, weight, fit, and maintainability. These factors affect soldiers' decisions about helmet use; therefore, rigorous research about soldiers' real-life experiences with helmets is critical to assessing a helmet's overall protective efficacy. This study compared soldiers' satisfaction and problem experience with the advanced combat helmet (ACH) and the personal armor system for ground troops (PASGT) helmet. Data were obtained from surveys of soldiers at Fort Bragg, North Carolina. Ninety percent of ACH users were satisfied overall with their helmet, but only 9.5% of PASGT users were satisfied (p < 0.001). The most frequently reported problems for the ACH involved malfunctioning helmet parts. The most frequently reported problems for the PASGT involved discomfort. This analysis indicated that there was a strong soldier preference for the ACH over the PASGT, which could enhance its already superior protective qualities. It also demonstrated the usefulness of soldiers' assessments of protective equipment.

Every day, workers in many locales are injured because they did not wear adequate eye and face protection. Or because they wore inadequate protection, the result of being poorly trained or not trained at all. Eye and face protection is required in workplaces ranging from paint booths and car repair shops to foundries, welding operations, and chemical manufacturing. Experts say the injuries are almost entirely preventable through adequate training and proper protection. OSHA has published non-mandatory guidelines for assessing hazards and selecting eye and face protection. OSHA points out that care should be taken to recognize the possibility of multiple and simultaneous exposure to a variety of hazards. Adequate protection against the highest level of each of the hazards should be provided. Protective devices do not provide unlimited protection.

Safety helmets are essential in various indoor and outdoor workplaces, such as metallurgical high-temperature operations and high-rise building construction, to avoid injuries and ensure safety in production. However, manual supervision is costly and prone to lack of enforcement and interference from other human factors. Moreover, small target object detection frequently lacks precision. Improving safety helmets based on the helmet detection algorithm can address these issues and is a promising approach. In this study, we proposed a modified version of the YOLOv5s network, a lightweight deep learning-based object identification network model. The proposed model extends the YOLOv5s network model and enhances its performance by recalculating the prediction frames, utilizing the IoU metric for clustering, and modifying the anchor frames with the K-means++ method. The global attention mechanism (GAM) and the convolutional block attention module (CBAM) were added to the YOLOv5s network to improve its backbone and neck networks. By minimizing information feature loss and enhancing the representation of global interactions, these attention processes enhance deep learning neural networks’ capacity for feature extraction. Furthermore, the CBAM is integrated into the CSP module to improve target feature extraction while minimizing computation for model operation. In order to significantly increase the efficiency and precision of the prediction box regression, the proposed model additionally makes use of the most recent SIoU (SCYLLA-IoU LOSS) as the bounding box loss function. Based on the improved YOLOv5s model, knowledge distillation technology is leveraged to realize the light weight of the network model, thereby reducing the computational workload of the model and improving the detection speed to meet the needs of real-time monitoring. The experimental results demonstrate that the proposed model outperforms the original YOLOv5s network model in terms of accuracy (Precision), recall rate (Recall), and mean average precision (mAP). The proposed model may more effectively identify helmet use in low-light situations and at a variety of distances.

Electronic point scoring systems (PSS) for vests are heavily relied upon in taekwondo. However, no classification and assessment of legal and illegal taekwondo techniques exist. This is also referred to as hit-validation and the objective of this research is to create an electronic helmet (eHelmet) for hit-validation. Three main studies were performed to achieve this objective: Robustness Testing, Sensor Placement and Classification of Impacts to the head. The first two studies are preliminary to the main Classification of Impacts study. This is needed as no data sets using an IMU are currently available for taekwondo. Robustness Testing: proved that IMU can in-fact be used in the inherently harsh environments of taekwondo with a linear response. The calculated response for the IMU is: f(x) = mx + b, where m is 0.2947 and b is 1.499 (accelerometer) and f(x) = mx + b, where m is 28.33 and b is 84.8 (gyroscope). Sensor Placement: Qualitatively and quantitatively concluded the ideal location for the sensor and electronics is indeed the back of the head, based on durability, cost, human factors, and signal quality. Classification of Impacts: IMU classified real-world impacts with 90% accuracy. The two classes were roundhouse kick (legal) and punch (illegal). An eHelmet using an IMU is capable of classifying impacts with high accuracy. The benefit of our system includes low cost, lightweight algorithm for on-device computing (edge computing), and real-time classification. Furthermore, it possesses all the safety requirements of current protective headgear.

This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the convolutional block attention module (CBAM) to bolster the model’s sensitivity to key features, thereby enhancing detection accuracy. To address potential performance degradation issues associated with the complete intersection over union (CIoU) loss function in the original model, we implement the modified penalty-decay intersection over union (MPDIoU) loss function to achieve more stable and precise bounding box regression. Furthermore, considering the original YOLOv5s model’s large parameter count, we adopt a lightweight design using the MobileNetV3 architecture and replace the original squeeze-and-excitation (SE) attention mechanism with CBAM, significantly reducing computational complexity. These improvements reduce the model’s parameters from 15.7 GFLOPs to 5.7 GFLOPs while increasing the mean average precision (mAP) from 82.34% to 91.56%, demonstrating its superior performance and potential value in practical industrial applications.

This case series study evaluates head and neck injuries caused by electric scooters in Paris, 2017-2019, focusing on user behavior and injury types.

Purpose Despite growing evidence regarding nonsynostotic plagiocephaly and their repercussions on motor development, there is little evidence to support the use of manual therapy as an adjuvant option. The aim of this study was to evaluate the effects of a therapeutic approach based on manual therapy as an adjuvant option on treatment duration and motor development in infants with severe nonsynostotic plagiocephaly. Methods This is a randomised controlled pilot study. The study was conducted at a university hospital. Forty-six infants with severe nonsynostotic plagiocephaly (types 4–5 of the Argenta scale) referred to the Early Care and Monitoring Unit were randomly allocated to a control group receiving standard treatment (repositioning and an orthotic helmet) or to an experimental group treated with manual therapy added to standard treatment. Infants were discharged when the correction of the asymmetry was optimal taken into account the previous clinical characteristics. The outcome measures were treatment duration and motor development assessed with the Alberta Infant Motor Scale (AIMS) at baseline and at discharge. Results Asymmetry after the treatment was minimal (type 0 or 1 according to the Argenta scale) in both groups. A comparative analysis showed that treatment duration was significantly shorter ( p  < 0.001) in the experimental group (109.84 ± 14.45 days) compared to the control group (148.65 ± 11.53 days). The motor behaviour was normal (scores above the 16th percentile of the AIMS) in all the infants after the treatment. Conclusions Manual therapy added to standard treatment reduces the treatment duration in infants with severe nonsynostotic plagiocephaly.

To evaluate the 2003 repeal of Pennsylvania’s motorcycle helmet law, we assessed changes in helmet use and compared motorcycle-related head injuries with non-head injuries from 2001–2002 to 2004–2005. Helmet use among riders in crashes decreased from 82% to 58%. Head injury deaths increased 66%; nonhead injury deaths increased 25%. Motorcycle-related head injury hospitalizations increased 78% compared with 28% for nonhead injury hospitalizations. Helmet law repeals jeopardize motorcycle riders. Until repeals are reversed, states need voluntary strategies to increase helmet use.

BACKGROUND: Recreational use of small-wheeled vehicles (SWVs), which include skateboards, longboards, nonmotorized scooters, ice skates, and roller skates or rollerblades, results in numerous injuries in the United States. OBJECTIVE: To describe the nature and severity of traumatic brain injuries (TBIs) that result from the use of SWVs in Utah. METHODS: Patients who were admitted to any Utah hospital after a SWV-related injury from 2001 through 2010 were identified from the Utah State Trauma Registry. Patients who sustained TBI were identified by International Classification of Diseases, Ninth Revision, codes. RESULTS: Of 907 patients admitted with SWV injury, 392 (43%) had a TBI (85% male). Their mean age was 19.8 ± 0.5 years, including 234 (60%) aged ⩽18 and 119 (30%) aged 19 to 29. Most patients sustained TBI while using a skate- or longboard (87%). Mean Glasgow Coma Scale score in the emergency department was 12.8 ± 0.2. Thirty-nine percent were admitted to an intensive care unit, and 6% (23) underwent emergent neurosurgical intervention. Thirty-three (8.4%) patients had a concussion; the rest had nonoperative intracranial hemorrhage. Among patients for whom helmet use data were available, 8 out of 291 (2.7%) patients with TBI were wearing a helmet, whereas 24 out of 190 (12.6%) non-TBI patients were wearing helmets (P < .001). Overall mortality was higher in TBI patients than in non-TBI patients (2.3% vs 0.2%, P = .003). CONCLUSION: Young people, especially males, who ride SWVs in Utah are at risk for serious TBI, admission to the intensive care unit, neurosurgical intervention, and death. Helmet use in these patients is likely rare, but may reduce the risk of TBI and death.

Mountain biking is increasing in popularity worldwide. The injury patterns associated with elite level and competitive mountain biking are known. This study analysed the incidence, spectrum and risk factors for injuries sustained during recreational mountain biking.The injury rate was 1.54 injuries per 1000 biker exposures. Men were more commonly injured than women, with those aged 30–39 years at highest risk. The commonest types of injury were wounding, skeletal fracture and musculoskeletal soft tissue injury. Joint dislocations occurred more commonly in older mountain bikers. The limbs were more commonly injured than the axial skeleton. The highest hospital admission rates were observed with head, neck and torso injuries. Protective body armour, clip-in pedals and the use of a full-suspension bicycle may confer a protective effect.