Search Results (3)

Background/Objectives: Equestrian activities can result in spine injuries. Most studies are from single centers, and none use a national database. It was the purpose of this study to describe the demographics, injury mechanisms, and types of equestrian-associated spinal injuries using a US national ED database. Methods: The National Electronic Injury Surveillance System database was queried for equestrian-related spine injuries from 2000–2023. ED disposition was categorized as discharged or not discharged. Statistical analyses accounted for the weighted, stratified nature of the data to obtain national estimates. Results: There were an estimated 54,830 patients, having an average age of 42 years. Most were female (73.6%) and White (93.7%); one-half (51.1%) were not discharged from the ED. The spine level was the lumbar (49.1%), thoracic (24.4%), sacrococcygeal (15.5%), and cervical (11.0%) spine. Multiple spine fractures occurred in 4.0%. A simple fall off a horse occurred in 53.6% of the injuries, and the patient was bucked/thrown/kicked off the horse in 39.7%. Neurologic injury was rare (1.8%). Hospital admission was highest in the cervical group (74.3%) and lowest in the sacrococcygeal group (33.5%). The cervical group had the highest percentage of males (43.7%) compared to the thoracic, lumbar, and sacrococcygeal groups (22.8%, 27.3%, 16.8%, respectively). There were proportionally fewer females in those over 50 years of age, where the male percentage was 11.7%, 25.6%, and 31.6% for those <18 years, 18–50 years, and >50 years old, respectively. Conclusions: This large study can be used as baseline data to evaluate further changes in equestrian injuries, especially the impact of further prevention strategies, education protocols, and legislative/governmental regulations of public equestrian localities. Full article

Objects thrown from tall buildings in communities are characterized by their small size, inconspicuous features, and high speed. Existing algorithms for detecting such objects face challenges, including excessive parameters, overly complex models that are difficult to implement, and insufficient detection accuracy. This study proposes a lightweight detection model for objects thrown from tall buildings in communities, named S-YOLOv5, to address these issues. The model is based on the YOLOv5 algorithm, and a lightweight convolutional neural network, Enhanced ShuffleNet (ESNet), is chosen as the backbone network to extract image features. On this basis, the initial stage of the backbone network is enhanced and the simplified attention module (SimAM) attention mechanism is added to utilize the rich position information and contour information in the shallow feature map to improve the detection of small targets. For feature fusion, the sparsely connected Path Aggregation Network (SCPANet) module is designed to use sparsely connected convolution (SCConv) instead of the regular convolution of the Path Aggregation Network (PANet) to fuse features efficiently. In addition, the model uses the normalized Wasserstein distance (NWD) loss function to reduce the sensitivity of positional bias. The accuracy of the model is further improved. Test results from the self-built objects thrown from tall buildings dataset show that S-YOLOv5 can detect objects thrown from tall buildings quickly and accurately, with an accuracy of 90.2% and a detection rate of 34.1 Fps/s. Compared with the original YOLOv5 model, the parameters are reduced by 87.3%, and the accuracy and rate are improved by 0.8% and 63%, respectively. Full article

Industry 4.0 smart manufacturing systems are equipped with sensors, smart machines, and intelligent robots. The automated in-plant transportation of manufacturing parts through throwing and catching robots is an attempt to accelerate the transportation process and increase productivity by the optimized utilization of in-plant facilities. Such an approach requires intelligent tracking and prediction of the final 3D catching position of thrown objects, while observing their initial flight trajectory in real-time, by catching robot in order to grasp them accurately. Due to non-deterministic nature of such mechanically thrown objects’ flight, accurate prediction of their complete trajectory is only possible if we accurately observe initial trajectory as well as intelligently predict remaining trajectory. The thrown objects in industry can be of any shape but detecting and accurately predicting interception positions of any shape object is an extremely challenging problem that needs to be solved step by step. In this research work, we only considered spherical shape objects as their3D central position can be easily determined. Our work comprised of development of a 3D simulated environment which enabled us to throw object of any mass, diameter, or surface air friction properties in a controlled internal logistics environment. It also enabled us to throw object with any initial velocity and observe its trajectory by placing a simulated pinhole camera at any place within 3D vicinity of internal logistics. We also employed multi-view geometry among simulated cameras in order to observe trajectories more accurately. Hence, it provided us an ample opportunity of precise experimentation in order to create enormous dataset of thrown object trajectories to train an encoder-decoder bidirectional LSTM deep neural network. The trained neural network has given the best results for accurately predicting trajectory of thrown objects in real time. Full article