Naturalistic driving studies (NDS) allow researchers to discreetly observe everyday, real-world driving to better understand the risk factors that contribute to hazardous situations. In particular, NDS designs provide high ecological validity in the study of driver distraction. With increasing dataset sizes, current best practice of manually reviewing videos to classify the occurrence of driving behaviours, including those that are indicative of distraction, is becoming increasingly impractical. Current statistical solutions underutilise available data and create further epistemic problems. Similarly, technical solutions such as eye-tracking often require dedicated hardware that is not readily accessible or feasible to use. A computer vision solution based on open-source software was developed and tested to improve the accuracy and speed of processing NDS video data for the purpose of quantifying the occurrence of driver distraction. Using classifier cascades, manually-reviewed video data from a previously published NDS was reanalysed and used as a benchmark of current best practice for performance comparison. Two software coding systems were developed - one based on hierarchical clustering (HC), and one based on gender differences (MF). Compared to manual video coding, HC achieved 86 percent concordance, 55 percent reduction in processing time, and classified an additional 69 percent of target behaviour not previously identified through manual review. MF achieved 67 percent concordance, a 75 percent reduction in processing time, and classified an additional 35 percent of target behaviour not identified through manual review. The findings highlight the improvements in processing speed and correctly classifying target behaviours achievable through the use of custom developed computer vision solutions. Suggestions for improved system performance and wider implementation are discussed.
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http://dx.doi.org/10.1016/j.aap.2014.06.007 | DOI Listing |
Vet Res
January 2025
Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
Cranioventral pulmonary consolidation (CVPC) is a common lesion observed in the lungs of slaughtered pigs, often associated with Mycoplasma (M.) hyopneumoniae infection. There is a need to implement simple, fast, and valid CVPC scoring methods.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Optometry, College of Medicine and Health Sciences, Comprehensive Specialized Hospital, University of Gondar, Gondar, Ethiopia.
Baground: Cataract is a major public health concern and the leading cause of blindness and low vision in Ethiopia. However, no studies have been conducted to assess the prevalence of cataract and associated factors among adult diabetic patients in the study area. Therefore, this study aimed to assess the prevalence of cataract and associated factors among adult diabetic patients in Northwest Ethiopia.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
January 2025
From the Orthopedic Data Innovation Lab (ODIL), Hospital for Special Surgery (A.M.L.S., M.A.F.), Department of Radiology and Imaging, Hospital for Special Surgery Centre (E.E.X, Z.I, E.T.T, D.B.S, J.L.C)and Department of Population Health Sciences, Weill Cornell Medicine (M.A.F), New York, New York, USA.
Background And Purpose: To train and evaluate an open-source generative adversarial networks (GANs) to create synthetic lumbar spine MRI STIR volumes from T1 and T2 sequences, providing a proof-of-concept that could allow for faster MRI examinations.
Materials And Methods: 1817 MRI examinations with sagittal T1, T2, and STIR sequences were accumulated and randomly divided into training, validation, and test sets. GANs were trained to create synthetic STIR volumes using the T1 and T2 volumes as inputs, optimized using the validation set, then applied to the test set.
Neuroscience
January 2025
Department of Computer Engineering, Faculty of Engineering, Igdir University, 76000, Igdir, Turkey. Electronic address:
Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates and long-term disabilities. Early diagnosis, particularly within the first hours, is crucial for preventing irreversible damage and improving patient outcomes. Although neuroimaging techniques like magnetic resonance imaging (MRI) have advanced significantly, traditional methods often fail to fully capture the complexity of brain lesions.
View Article and Find Full Text PDFMicrosc Microanal
January 2025
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin 14195, Germany.
In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for nonautomated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model.
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