Embedded fatigue state detection system and method

Abstract

The invention provides an embedded fatigue state detection system and method, and the system comprises an image obtaining module which is used for dynamically capturing the video of a learner througha camera, converting the video into a frame image according to a preset frame interval, and carrying out the normalization processing; a data processing module used for positioning a human face from the frame image, and then positioning and intercepting eye and mouth images, identifying the opening and closing states of the eyes and the mouth through the trained convolutional neural network, calculating the frame number frequency of eye closing and yawning in the convolutional neural network by adopting Parclos and Fom rules, and comparing the frame number frequency with a preset joint judgment threshold to judge whether the target is tired or not; and an output decision module used for controlling a loudspeaker and a display screen to display according to the judgment result of the data processing module. The system and the method provided by the invention are high in robustness, good in practicability and easy for product transformation.

Description

technical field [0001] The invention belongs to the field of fatigue state detection, and in particular relates to an embedded learning fatigue state detection system and method based on a convolutional neural network. Background technique [0002] With the rapid development of information technology, more and more learners are able to use network resources for online learning and to collect materials and use tools to learn independently. In this environment, the lack of supervision on the learning status may reduce efficiency and make it easier fatigue. Fatigue status can effectively reflect learners' interest and depth of understanding of the current content. Therefore, detecting learning fatigue status is of great value and significance for reminding learners to concentrate and adjust their status. [0003] At present, the fatigue detection methods mainly use physiological sensors to detect physiological change indicators such as EEG, ECG, heart rate, and respiration; d...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems in the existing fatigue state detection method that it is difficult to manually define the state characteristics, the algorithm is cumbersome, the system is complex, the recognition accuracy is low, and the applicability of the application scene is low, the present invention provides an embedded learning fatigue state detection based on deep learning technology The system and method, the convolutional neural network is introduced into the recognition method to learn and classify the facial features of the detection object, and the fatigue state detection is realized by combining the Perclos and Fom judgment rules

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