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Deep Learning-Based Fatigue Detection for Online Learners

Existing fatigue detection technologies are mainly used in fatigue driving detection. With continuous development of educational infrastructure, there is a trend to introduce fatigue detection technology into online classroom. This paper explores the fatigue detection technology under the online classroom environment. Firstly, the feature state recognition of eye opening and closing, mouth opening and closing and head pose is realized through face detection, deep learning and continuous head pose estimation. The user’s fatigue state is determined based on the frequencies of blinking, yawning and nodding. A time sequential slide window is introduced in order to prevent misjudgment cause by fatigue features of a user in a non-fatigue state. The experimental results illustrate that the fatigue detection accuracy of our system is higher than the traditional PERCLOS algorithm. In the future, issues such as the fatigue judgment model, more refined head posture detection and multi-modal feature design will be further investigated.