98%
921
2 minutes
20
Although many countries restrict the use of smartphones while driving, smartphones are utilized in various ways as there are limits to enforcement. Accordingly, efforts are made to determine the risks of novice drivers with low safety awareness and higher risk. This study observed and analyzed changes in visual attention and driving risks according to the way smartphones are used while driving and the scientific relationship between the 2 variables. Forty-five novice drivers were asked to simultaneously perform 4 types of tasks during a driving simulation: hand-held calls, portable hands-free (Bluetooth) calls, sending messages, and not using smartphones. In this process, visual attention was tested using an eye tracker, and the driving function was examined using scenario driving results. Afterward, the differences in visual attention and driving function by task category and the correlation between the 2 variables were analyzed. Significant differences were confirmed in the following categories of novice drivers' smartphone use while driving: eye blink duration, eye fixation frequency, average eye fixation duration, frequency of saccadic eye movement, average saccade duration, and amplitude and speed of saccade. Additionally, there was a significant relationship between driving risk in speeding rate, centerline crossing rate, road edge excursion rate, average deviation rate, number of off-road accidents, and the number of collision accidents. Lastly, visual attention indices had different significant positive or negative correlations with driving functions. When novice drivers use smartphones while driving compared to when they do not use smartphones, changes in visual attention characteristics in the number and duration of eye blinks, eye fixations, and saccades increased the risk of accidents due to deceleration and lane departure. In particular, the risk increased the most when sending messages, and the risk of accidents continued although the increased burden due to smartphone use was compensated for by slowing down the speed. We hope that the findings of this study will be actively used in efforts to change novice drivers' traffic safety attitudes while driving.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11608744 | PMC |
http://dx.doi.org/10.1097/MD.0000000000040764 | DOI Listing |
Arq Gastroenterol
September 2025
The Japanese Society of Internal Medicine, Editorial Department, Tokyo, Japan.
Background: This study aims to analyze research trends and emerging insights into gut microbiota studies from 2015 to 2024 through bibliometric analysis techniques. By examining bibliographic data from the Web of Science (WoS) Core Collection, it seeks to identify key research topics, evolving themes, and significant shifts in gut microbiota research. The study employs co-occurrence analysis, principal component analysis (PCA), and burst detection analysis to uncover latent patterns and the development trajectory of this rapidly expanding field.
View Article and Find Full Text PDFPLoS One
September 2025
School of Computer Science, CHART Laboratory, University of Nottingham, Nottingham, United Kingdom.
Background And Objective: Male fertility assessment through sperm morphology analysis remains a critical component of reproductive health evaluation, as abnormal sperm morphology is strongly correlated with reduced fertility rates and poor assisted reproductive technology outcomes. Traditional manual analysis performed by embryologists is time-intensive, subjective, and prone to significant inter-observer variability, with studies reporting up to 40% disagreement between expert evaluators. This research presents a novel deep learning framework combining Convolutional Block Attention Module (CBAM) with ResNet50 architecture and advanced deep feature engineering (DFE) techniques for automated, objective sperm morphology classification.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Community Ecology, Helmholtz Centre for Environmental Research - UFZ, Halle (Saale), Germany.
Pollination is essential for maintaining biodiversity and ensuring food security, and in Europe it is primarily mediated by four insect orders (Coleoptera, Diptera, Hymenoptera, Lepidoptera). However, traditional monitoring methods are costly and time consuming. Although recent automation efforts have focused on butterflies and bees, flies, a diverse and ecologically important group of pollinators, have received comparatively little attention, likely due to the challenges posed by their subtle morphological differences.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Thyroid eye disease (TED) is a prevalent autoimmune orbital disorder that can severely impair visual function and significantly diminish patients' quality of life. In recent years, several studies have attempted to automate TED diagnosis using optical coherence tomography (OCT) images. However, existing approaches primarily rely on convolutional neural networks (CNNs) combined with attention mechanisms and are mostly trained using traditional cross-entropy loss.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Accurate vascular segmentation is essential for coronary visualization and the diagnosis of coronary heart disease. This task involves the extraction of sparse tree-like vascular branches from volumetric space. However, existing methods have faced significant challenges due to discontinuous vascular segmentation and missing endpoints.
View Article and Find Full Text PDF