Deep Learning-Based Fatigue Detection for Online Learners

Chen, Yaxi; Zhao, Dinghao; He, Guixian

doi:10.1109/prai55851.2022.9904096

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Cited by 2 publications

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Real Time Detection of Learner Fatigue in E-Learning Environments through the Combination of Eye and Mouth Features

Abdolhosseinzadeh,

Montazer

2024

2024 11th International and the 17th National Conference on E-Learning and E-Teaching (ICeLeT)

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Real Time Detection of Learner Fatigue in E-Learning Environments through the Combination of Eye and Mouth Features

Abdolhosseinzadeh,

Montazer

2024

2024 11th International and the 17th National Conference on E-Learning and E-Teaching (ICeLeT)

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Drowsiness Sensing System of Driver Based on Behavioral Characteristics to Prevent Road Accidents Using RealTime Optimized Computer Vision

2023

jame

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A computer vision-based system called the Drowsiness Sensing Device using OpenCV was designed to identify driver drowsiness. The technology uses video frames from a camera positioned inside a car to identify different sleepiness indicators, including the length of eye closure and head position. The Eye Aspect Ratio (EAR), which aids in trying to assess drowsiness, is determined using the OpenCV library, which is also used to extract feature points and detect eye blinks. The system also has an alarm mechanism that sounds when a certain level of drowsiness is attained, alerting the driver to take the appropriate action. The proposed approach can be possibly employed to reduce the number of accidents occurred due to driver drowsiness. The suggested system is a real-time drowsiness sensing system that makes use of OpenCV to gauge a person's level of drowsiness. The technology employs a camera to take pictures of the driver's face, assessing the features like the mouth and eyes to determine how sleepy they are. The system can identify drowsiness by noticing changes in the eyes, such as drooping eyelids, and mouth movements, such as yawning. When the amount of drowsiness surpasses a predetermined threshold, the system informs the driver by assessing the photos using machine learning techniques. By prompting the driver to take a break, the proposed technology may help prevent accidents brought on by drowsy driving.

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Deep Learning-Based Fatigue Detection for Online Learners

Cited by 2 publications

References 3 publications

Real Time Detection of Learner Fatigue in E-Learning Environments through the Combination of Eye and Mouth Features

Real Time Detection of Learner Fatigue in E-Learning Environments through the Combination of Eye and Mouth Features

Drowsiness Sensing System of Driver Based on Behavioral Characteristics to Prevent Road Accidents Using RealTime Optimized Computer Vision

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