Real-time brain fatigue monitoring device based on deep learning and data processing method

Abstract

The invention discloses a real-time brain fatigue monitoring device based on deep learning and a data processing method, a portable forehead wearable device which has high integration and surrounds the head through an elastic soft belt is adopted, blood oxygen signals of the forehead lobe of the brain are collected in real time, and a current brain function activity topographic map is displayed in real time. Preprocessing the data, including removing motion artifacts, moving average and Butterworth band-pass filtering processing and a corrected Lambert-Beer law, and obtaining oxygenation blood red concentration variable quantity data and deoxidized hemoglobin concentration variable quantity data through data preprocessing; data analysis and data classification are carried out on the collected blood oxygen signals of the brain prefrontal lobe by using a CNN algorithm based on deep learning on the preprocessed data, a four-classification brain fatigue recognition task is carried out on the preprocessed data, classification labels are light fatigue, heavy fatigue, relaxation and concentration respectively, and finally the brain fatigue state of the tested object is obtained.

Description

technical field [0001] The invention relates to a near-infrared forehead brain imaging fatigue detection device. In particular, it relates to a deep learning-based prefrontal brain imaging device and data processing method. Background technique [0002] Due to the lack of vigilance and vigilance of drivers during driving, the proportion of traffic accidents caused by driving fatigue in society is getting higher and higher. Therefore, driving fatigue has become one of the main causes of road tragedies. Serious consequences can be caused by lack of awareness of potential accidents, lack of awareness of traffic conditions, and inability to control the vehicle. Recent statistics show that driver mental fatigue poses a devastating threat to the lives of drivers and other traffic offenders. Since drowsy driving and drunk driving have long been the most serious traffic problems affecting public safety, attempts to signal detect both conditions have existed for many years. For d...

AI Technical Summary

Problems solved by technology

These signals can be used to extract information about the degree of fatigue, but this information only reflects a certain aspect of the degree of human fatigue, and cannot fully measure the driver's fatigue degree

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