Driver vigilance detection for high-speed rail using fusion of multiple physiological signals and deep learning

Abstract

With the popularity of high-speed rail in China and with faster operation speeds, the safety of railways has also received more attention. This paper proposes a vigilance detection method for high-speed rail drivers based on wireless wearable multi-physiological signals fusion and deep learning. The intended method consists of three components: wireless wearable signal acquisition equipment, physiological signal preprocessing and driver vigilance detection. In the initial stage, a wireless wearable device based on open source brain–computer interfaces was used to collect electroencephalogram, electrocardiogram and electromyogram signals in the high-speed rail simulation environment. Secondly, linear filtering, fast independent component analysis and wavelet filtering are performed on the three kinds of signals, and reasonable slicing is performed to make a dataset. Finally, a convolutional recurrent neural network with channel attention mechanism and memory ability is proposed. Multiple physiological signals from wireless wearable devices are used to train the network. This network improves the ability to recognize the vigilance of drivers and verifies the effectiveness of the combination of squeeze-and-excitation block and long short-term memory with convolutional neural network. Furthermore, the vigilance detection effectiveness was evaluated under different signal combinations; the testing set verified the accuracy of the network as 98.11%. Results prove the feasibility of the high-speed rail driver vigilance detection method based on multiple physiological signals and deep learning, which can help to avoid high-speed rail accidents.