Sleep prediction method and device and storage medium

Abstract

The invention provides a sleepiness prediction method which comprises the following steps: step S100, acquiring an image of a detection target, and performing face detection and skin recognition, including detecting a target face area and extracting and clustering related skin areas; measuring and calculating an eye aspect ratio; s200, the clustering result of each skin area is processed; extracting and calculating an rPPG signal by comparing a clustering result with the RGB average value corresponding to each frame, and finally estimating a target heart rhythm through a fast Fourier transformmethod; and step S300, selecting optimal signals detected through different clustering results to estimate the target sleepiness level. According to the invention, the sleepiness of a driver can be objectively and truly reflected.

Description

technical field [0001] The invention relates to the technical field of biotechnology, in particular to a drowsiness prediction method and device for drivers. Background technique [0002] The existing method is to judge drowsiness by segmenting the monitoring image and comparing facial features, including the position of key points such as mouth and eyes, and to give early warning. Doppler radar and complex signal processing methods are used to obtain the The tester’s restless emotional activities, blink frequency and duration and other fatigue data are used to judge whether the testee is dozing off or falling asleep; the sleepiness can be judged by changing the positions of key points such as mouth and eyes over time, refer to The elements are relatively single, and cannot objectively and truly reflect the sleepiness of the testee, and are low in technicality, low in accuracy, and lagging. [0003] The term rPPG (remote Photo PlethysmoGraphy) involved in the present invent...

AI Technical Summary

Problems solved by technology

[0002] The existing method is to judge drowsiness by segmenting the monitoring image and comparing facial features, including the position of key points such as mouth and eyes, and to give early warning. Doppler radar and complex signal processing methods are used to obtain the The tester’s restless emotional activities, blink frequency and duration and other fatigue data are used to judge whether the testee is dozing off or falling asleep; the sleepiness can be judged by changing the positions of key points such as mouth and eyes over time, refer to The elements are relatively single, and cannot objectively and truly reflect the sleepiness of the testee, and are low in technicality, low in accuracy, and lagging

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