Fatigue detection and wake-up system based on eye movement parameter monitoring based on mod-net network

Abstract

The invention discloses a fatigue detection and wake-up promotion system based on MOD-Net network eye movement parameter monitoring, comprising: a glasses body; an image acquisition module for collecting side eye image data of a wearer; a wake-up promotion module; a power supply module ; Background processing module, which includes MOD‑Net network eye movement data analysis module, fatigue degree analysis module and main control module. The present invention can automatically determine the degree of human fatigue in real time through the analysis of eye movement data, and carry out early warning according to the level of human fatigue, and apply wake-up stimulation combined with sound, light and vibration, so as to reduce the degree of human fatigue and make the human body alert and stable. Improved operational ability; the present invention uses the MOD-Net network eye movement data analysis module to analyze eye movement data, which has better anti-noise, anti-rotation and anti-scale transformation capabilities, and can capture blurred eye areas and improve fatigue detection the accuracy rate.

Description

technical field [0001] The invention relates to the technical field of fatigue detection and awakening stimulation, in particular to a fatigue detection and awakening promotion system based on MOD-Net network eye movement parameter monitoring. Background technique [0002] With the development of random society, the automation level of the current work is improving. Various jobs require staff such as pilots to maintain a high degree of alertness and abundant energy and attention to ensure that high-end equipment is operated without errors and related operations are carried out smoothly. However, Continuous, high-intensity, non-scheduled operations affect the staff's judgment, decision-making and combat execution capabilities. Therefore, it is necessary to accurately evaluate the influence of temporal biorhythm and its functional state on related working ability under continuous working conditions, and provide scientific intervention to improve the working efficiency of relev...

AI Technical Summary

Problems solved by technology

The RCNN series algorithm uses the 2-stage method for target detection, which has high detection accuracy for multi-target and fine-grained targets, but it still has defects, such as its large amount of calculation, and cannot achieve real-time performance, etc.

And at present, the relevant equipment on the market is mainly focused on the research and development of driving fatigue detection devices, lack of research on related fields such as personal work ability improvement, and lack of portable wearable devices that integrate fatigue detection, early warning and wake-up stimulation. device

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