Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model.

Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more accurate drowsiness detection systems using behavioral features such as mouth and eye movement. This study proposes a deep neural network architecture for drowsiness detection employing a convolutional neural network (CNN) for driver drowsiness detection. Experiments involve using the DLIB library to locate key facial points to calculate the mouth aspect ratio (MAR). To compensate for the small dataset, data augmentation is performed for the 'yawning' and 'no_yawning' classes. Models are trained and tested involving the original and augmented dataset to analyze the impact on model performance. Experimental results demonstrate that the proposed CNN model achieves an average accuracy of 96.69%. Performance comparison with existing state-of-the-art approaches shows better performance of the proposed model.

Frailty assessment and impact of frailty on outcomes after transcatheter aortic valve replacement.

INTRODUCTION: Certain frail patients fail to achieve adequate functional or mortality benefit despite successful transcatheter aortic valve replacement (TAVR). Therefore, frailty assessment methods are becoming an important tool to identify and intervene on this high-risk patient subset for improving clinical outcomes. Areas covered: The authors provide an overview of frailty and frailty assessment tools being used in clinical practice and discuss the impact of frailty on the cardiac patients, particularly among the TAVR population. Expert commentary: Available evidence suggests that frailty assessment is critical for identifying patients at high risk of morbidity and mortality after TAVR procedures. However, there is lack of consensus for the best methodology to determine frailty and its optimal management in TAVR populations. Although, physical exercise is a commonly employed intervention to reduce frailty, a greater attention towards improving nutrition may convey more benefit than either intervention alone. Ongoing studies are investigating the benefits of a multicomponent approach to improve clinical outcomes in frail patients undergoing TAVR.