Evaluation of driver drowsiness based on respiratory metrics.

Background: The transition from alertness to drowsiness can cause considerable changes in the respiratory system, providing an opportunity to detect driver drowsiness.

Objective: The aim of this study was to determine which respiratory features indicate driver drowsiness and then use these features to classify the level of drowsiness and alertness.

Methods: Twenty male students (mean age 25.

A hybrid approach for driver drowsiness detection utilizing practical data to improve performance system and applicability.

Background: Numerous systems for detecting driver drowsiness have been developed; however, these systems have not yet been widely used in real-time.

Objective: The purpose of this study was to investigate at the feasibility of detecting alert and drowsy states in drivers using an integration of features from respiratory signals, vehicle lateral position, and reaction time and out-of-vehicle ways of data collection in order to improve the system's performance and applicability in the real world.

Methods: Data was collected from 25 healthy volunteers in a driving simulator-based study.

Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection.

Many endeavors have sought to develop countermeasure techniques as enhancements on Automatic Speaker Verification (ASV) systems, in order to make them more robust against spoof attacks. As evidenced by the latest ASVspoof 2019 countermeasure challenge, models currently deployed for the task of ASV are, at their best, devoid of suitable degrees of generalization to unseen attacks. A joint improvement of components of ASV spoof detection systems including the classifier, feature extraction phase, and model loss function may lead to a better detection of attacks by these systems.