Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2017-03, Vol.17 (3), p.486
Main Authors: Zhang, Xiaoliang, Li, Jiali, Liu, Yugang, Zhang, Zutao, Wang, Zhuojun, Luo, Dianyuan, Zhou, Xiang, Zhu, Miankuan, Salman, Waleed, Hu, Guangdi, Wang, Chunbai
Format: Article
Language:English
Subjects:
Bluetooth
brain-computer interface
Computer simulation
Electroencephalography
fatigue detection system
Fatigue tests
High speed rail
high-speed train safety
Human-computer interface
Locomotives
Personal computers
Railroad accidents & safety
Safety management
Support vector machines
Trains
vigilance detection
Warning systems
Wearable technology
wireless wearable
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Summary:The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver's brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.
ISSN:1424-8220
1424-8220
DOI:10.3390/s17030486