Fine-grained human body fatigue detection method based on embedded device and device thereof

Abstract

The invention discloses a fine-grained human body fatigue detection method based on an embedded device and device. The human body fatigue detection device is built by adopting an infrared light sensing device and an MCU control unit; collecting human eye state information through infrared light sensing equipment by utilizing the light absorption effect of human eye pupils; setting ADC conversion parameters of the MCU control unit, and converting an analog signal into a digital signal; carrying out initialization setting on each photodiode to obtain a reference value of a human eye opening state, and carrying out filtering and smoothing preprocessing on reference value data; processing the preprocessed data by using a CUSUM algorithm, and determining a potential blink position on a time domain; using two matching methods to judge a real blinking behavior; and estimating and judging the fatigue state by using a PERCLOS method to complete human body fatigue detection. According to the invention, the state information, the eye closing duration and the blinking duration of the human eyes can be collected more accurately, and long-term monitoring of fatigue in daily wearing is satisfied.

Description

technical field [0001] The invention belongs to the technical field of human fatigue detection, and in particular relates to a fine-grained human fatigue detection method and equipment based on an embedded device. Background technique [0002] Human fatigue detection has always been a research hotspot in many industries: In the transportation industry, the number of accidents caused by fatigue of automobile drivers and railway locomotive drivers will increase every year with the increase of vehicles and the continuous extension of locomotive traffic. At the same time, in the medical field, fatigue is the leading signal of some health problems, such as cancer, addictive mental diseases, etc., through daily monitoring of patient fatigue, so as to assist doctors to make timely early warning and judgment. There are many existing fatigue detection methods, and the common ones can be divided into methods based on human physical signals and methods based on human behavior signals. ...

AI Technical Summary

Problems solved by technology

However, the image method is not mainly used for the extraction of human eye state information after all, so the image method will be limited to: the interference of ambient light in daily applications, the detected person needs to be in the state of facing the camera for a long time, and the image accuracy is not high And the amount of calculation is huge, the price of high fps camera is too high, etc.

These limitations have caused the image to use human eye information to perform fatigue detection in the laboratory, which is difficult to promote.

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