Electroencephalogram and electrocardiogram-based fatigue detection method of electrocardiogram sensor embedded into steering wheel

Abstract

The invention discloses an electroencephalogram and electrocardiogram-based fatigue detection method of an electrocardiogram sensor embedded into a steering wheel. The method performs fatigue detection by constructing a product fuzzy convolutional network. The method specifically comprises the following steps: S1, acquiring electrocardiogram data through an electrocardiogram detection chip, and acquiring electroencephalogram time series data by using an electroencephalograph; S2, processing the electroencephalogram time series data by adopting a fuzzy neural network which contains a laminationand has feedback, and acquiring electroencephalogram characteristic; S3, establishing a depth feature extraction network based on a one-dimensional convolutional neural network framework to extract fatigue features of the electrocardiogram data, and generating an electrocardiogram feature sequence; S4, designing a fusion network, inputting the electrocardiogram characteristic sequence and the electroencephalogram characteristic at the same time, fusing the two signals together, and giving a prediction value; and S5, performing optimizing by using an adaptive moment estimation algorithm, and training a network model. The method can reduce the noise and improve the detection precision, the limitation of the fuzzy neural network on the feature dimension of the input data is reduced by introducing the lamination, and the accuracy of the classification result is improved.

Description

technical field [0001] The invention belongs to the field of fatigue detection, in particular to a fatigue detection method based on electroencephalogram and electrocardiogram with an electrocardiogram sensor embedded in a steering wheel. Background technique [0002] With the rapid increase in the number of cars, safe driving has never been more of a concern. Unfortunately, providing real-time feedback to the driver, and even changing the state of automation through intelligent analysis of the environment, is a very expensive task. Nevertheless, predicting the driver's latent state can alleviate these problems. Fatigue can have a major impact on driving and can affect a person's ability to drive safely. As our roads get busier, fatigue fractures have become a problem that needs to be addressed. Research shows that fatigue is the root cause of as many as 35% to 45% of traffic accidents. [0003] If there is an effective auxiliary system for detecting driver drowsiness, i...

AI Technical Summary

Problems solved by technology

In this case, the fnn-based model will lose more useful information and it is difficult to obtain accurate classification results

The quality of the EEG signal is often unstable and noisy, and when the subject talks, blinks or shakes the head, the obtained EEG signal contains a large number of features that are not related to fatigue, so it is difficult to detect

Images