Fatigue detection method based on EEG and ECG with ECG sensor embedded in the steering wheel

Abstract

The invention discloses a fatigue detection method based on electroencephalogram and electrocardiogram in which the steering wheel is embedded with an electrocardiogram sensor. Fatigue detection is performed by constructing a product fuzzy convolution network, which specifically includes: S1. Obtaining electrocardiogram data through an electrocardiogram detection chip, and using The electroencephalograph obtains EEG time series data; S2, uses a fuzzy neural network with multilayer feedback to process EEG time series data, and obtains EEG features; S3, builds a deep network based on a one-dimensional convolutional neural network framework The feature extraction network extracts the fatigue features of the ECG data and generates the ECG feature sequence; S4. Design the fusion network, input the ECG feature sequence and the EEG feature at the same time, fuse the two signals together, and give the predicted value; S5 , Use the adaptive moment estimation algorithm to optimize and train the network model. It can reduce noise and improve detection accuracy. The introduction of multilayer reduces the limitation of fuzzy neural network on the feature dimension of input data and improves the accuracy of classification results.

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

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